Showing 1–50 of 115 results for author: Tosi, N

How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits

Authors: Prabhjot Singh, Adel N. Toosi, Rajkumar Buyya

Abstract: Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of cuts - the dominant runtime cost in prior work. We ask whether, for machine-learning tasks, this step is necessary, and replace it with late fusion: each subcircuit is trained and m… ▽ More Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of cuts - the dominant runtime cost in prior work. We ask whether, for machine-learning tasks, this step is necessary, and replace it with late fusion: each subcircuit is trained and measured independently, and a small classical head combines their outputs - a linear-cost, decision-level combination borrowed from multimodal learning. To characterize the trade-off we introduce a quantumness dial $Q$, a tunable reconstruction budget interpolating from pure fusion to full reconstruction, and a cut-entanglement diagnostic that indicates how much reconstruction a task needs (Spearman $ρ=0.59$ over $104$ runs). Across synthetic and standard datasets, independently trained late fusion matches full reconstruction accuracy within $0.04$ at every point of the controlled sweep and on every classical benchmark, at exponentially lower cost; it is also markedly more robust to shot and device noise. Controlled entangled-data experiments locate the boundary where fusion must fail. We do not claim advantage over classical machine learning - consistent with recent benchmarking, quantum offers no accuracy edge on these datasets. Late fusion is thus an efficient, noise-robust, self-characterizing alternative to reconstruction for circuit-cutting QML. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 11 pages, 6 figures. Includes technical appendix

A Bayesian approach to the long-baseline neutrino oscillation sensitivity of DUNE

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, K. Adhikari, C. Adriano, K. Agudelo-Jaramillo, F. Akbar, F. Alemanno, N. S. Alex, L. Aliaga Soplin, A. Alqaisi, O. Alterkait, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, R. M. Amarinei, P. Amedo , et al. (1262 additional authors not shown)

Abstract: The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is evaluated using a Bayesian Markov Chain Monte Carlo (MCMC) approach. This analysis uses the same underlying sensitivity inputs as previous DUNE studies [Eur. Phys. J. C 80, 978 (2020)], and therefore does not present updated DUNE sensitivities, but instead explores the additional inferences accessible usi… ▽ More The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is evaluated using a Bayesian Markov Chain Monte Carlo (MCMC) approach. This analysis uses the same underlying sensitivity inputs as previous DUNE studies [Eur. Phys. J. C 80, 978 (2020)], and therefore does not present updated DUNE sensitivities, but instead explores the additional inferences accessible using a Bayesian approach. We present four-dimensional posterior probability distributions of the oscillation parameters, highlighting the breadth of correlation in the parameter space of interest, especially between $\sin^2 θ_{23}$ and $\sin^2 θ_{13}$. We exploit the flexibility of the Bayesian framework to incorporate parameter constraints post hoc and assess the impact of applying a reactor short-baseline $θ_{13}$ constraint. A significant increase in the sensitivity to the $θ_{23}$ octant is found when including the constraint. Posterior distributions of derived quantities can be easily constructed from MCMC results. This work presents the first study of DUNE's sensitivity to the Jarlskog invariant, $J$, a quantity that provides a parametrisation-independent measure of charge-parity violation in the leptonic sector. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: 20 pages, 5 figures

Report number: FERMILAB-PUB-26-0548-LBNF

Operation and performance of ProtoDUNE Dual Phase liquid argon time projection chamber

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, K. Adhikari, C. Adriano, K. Agudelo-Jaramillo, F. Akbar, F. Alemanno, N. S. Alex, L. Aliaga Soplin, A. Alqaisi, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, R. Amarinei , et al. (1341 additional authors not shown)

Abstract: ProtoDUNE-DP was the largest ever built Liquid Argon Time Projection Chamber (LArTPC) operating in Dual-Phase (DP) mode, with a liquid target and charge read-out placed in the gas. It had an active volume of $6\times6\times6$\,m$^3$ corresponding to an active mass of 300\,t (total LAr mass of 720\,t), constructed at the CERN Neutrino Platform and took data from 2019 to 2020 with cosmic muons. In P… ▽ More ProtoDUNE-DP was the largest ever built Liquid Argon Time Projection Chamber (LArTPC) operating in Dual-Phase (DP) mode, with a liquid target and charge read-out placed in the gas. It had an active volume of $6\times6\times6$\,m$^3$ corresponding to an active mass of 300\,t (total LAr mass of 720\,t), constructed at the CERN Neutrino Platform and took data from 2019 to 2020 with cosmic muons. In ProtoDUNE-DP the electric drift field is oriented in the vertical direction, causing the electrons to drift vertically towards the anode at the top. The ionization charge is then extracted into the gaseous argon above the liquid surface, amplified by Townsend avalanches, and collected by the charge readout planes. The detector experienced significant technical problems affecting the long-term operation of the Charge Readout Planes, formed by the Large Electron Multipliers, but other critical segments demonstrated required performance including the delivery of -300 kV to the TPC cathode, verification of replaceable charge read-out electronics, and operation of the photon detection system. ProtoDUNE-DP experience resulted in improved designs of the Vertical Drift LArTPC. △ Less

Submitted 21 July, 2026; v1 submitted 17 July, 2026; originally announced July 2026.

Comments: 103 pages, 66 figures

Report number: FERMILAB-PUB-26-0466-LBNF

Coordinated Scheduling for MoE LLM Serving

Authors: Yifan Sun, Zhexiang Zhang, Jiantong Jiang, Gholamreza Haffari, Minxian Xu, Feng Liu, Rajkumar Buyya, Adel N. Toosi

Abstract: Serving Mixture-of-Experts (MoE) large language models (LLMs) is challenging because dynamic request workloads interact with sparse expert routing, creating both data-parallel (DP) engine imbalance and expert-level hotspots. Existing LLM serving systems typically make these decisions in isolation: frontend schedulers route requests using coarse request counters, while backend expert balancers rely… ▽ More Serving Mixture-of-Experts (MoE) large language models (LLMs) is challenging because dynamic request workloads interact with sparse expert routing, creating both data-parallel (DP) engine imbalance and expert-level hotspots. Existing LLM serving systems typically make these decisions in isolation: frontend schedulers route requests using coarse request counters, while backend expert balancers rely mainly on aggregate expert activation counts. This separation prevents the serving system from reacting to fine-grained engine pressure, backend MoE pressure, and source-dependent expert traffic. To address this gap, we propose Gimbal, a coordinated cross-level scheduling system for efficient MoE-based LLM serving. First, Gimbal presents a fine-grained DP-engine scheduler that uses online backend pressure signals, including key-value (KV) cache usage, remaining prefill work, queue pressure, and MoE expert pressure, to dispatch requests away from overloaded engines. Inside each engine, Gimbal further applies a lightweight prefill-aware queue ordering policy with aging to reduce head-of-line blocking without output-length prediction. Second, Gimbal extends expert load balancing with online source-DP-to-expert routing statistics and uses a heuristic guided by a mixed-integer nonlinear program (MINLP) to place experts while jointly considering expert load, source-aware communication, and migration stability. Our evaluation shows that Gimbal reduces average Time To First Token (TTFT) by 42.9% and average Time Per Output Token (TPOT) by 33.3% compared with the state-of-the-art serving system vLLM, while improving high-load request throughput by 3.0%. △ Less

Submitted 13 June, 2026; originally announced June 2026.

GraphFlash: Enabling Fast and Elastic Graph Processing on Serverless Infrastructure

Authors: Chen Zhao, Parsa Poorsistani, Mohammad Goudarzi, Tawfiq Islam, Adel N. Toosi

Abstract: Graph processing systems are essential for analyzing large-scale data with complex relationships, yet most existing frameworks rely on statically provisioned clusters, resulting in poor elasticity and inefficient resource utilization under dynamic workloads. Serverless computing offers automatic scaling and fine-grained billing, but existing serverless graph systems suffer from performance limitat… ▽ More Graph processing systems are essential for analyzing large-scale data with complex relationships, yet most existing frameworks rely on statically provisioned clusters, resulting in poor elasticity and inefficient resource utilization under dynamic workloads. Serverless computing offers automatic scaling and fine-grained billing, but existing serverless graph systems suffer from performance limitations due to inefficient state management and high communication overhead through external storage. We present GraphFlash, a fast and elastic graph processing framework built on serverless infrastructure. GraphFlash adopts a subgraph-centric programming model and leverages shared external storage for coordination and communication, enabling stateless, fine-grained function execution. It supports two execution modes: rotating mode for resource-constrained environments and pinned mode for higher performance when resources are sufficient. To address serverless limitations, GraphFlash introduces system-level optimizations, including partition-aware key aggregation, intra-function partition co-location, and superstep-aware activation. Across multiple graph algorithms and datasets, GraphFlash outperforms existing serverless-compatible systems by up to 127x in execution time and reduces resource consumption by up to 98% under higher-resource configurations, while matching the performance of traditional distributed frameworks on large workloads. Even with limited resources, it achieves up to 48x speedup and 99.97% cost reduction over prior serverless solutions, demonstrating that GraphFlash makes serverless graph processing practical and performant. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Charge readout electronics for the DUNE horizontal drift far detector: design and performance in ProtoDUNE-HD

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, K. Adhikari, C. Adriano, K. Agudelo-Jaramillo, F. Akbar, F. Alemanno, N. S. Alex, L. Aliaga Soplin, A. Alqaisi, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, R. Amarinei , et al. (1346 additional authors not shown)

Abstract: DUNE (Deep Underground Neutrino Experiment) is a long-baseline neutrino oscillation experiment currently under construction, whose far detectors will be the largest liquid argon time projection chambers ever built. This detector design calls for custom-built cryogenic front-end electronics to meet its performance requirements. This paper describes the charge readout electronics that will be used i… ▽ More DUNE (Deep Underground Neutrino Experiment) is a long-baseline neutrino oscillation experiment currently under construction, whose far detectors will be the largest liquid argon time projection chambers ever built. This detector design calls for custom-built cryogenic front-end electronics to meet its performance requirements. This paper describes the charge readout electronics that will be used in the DUNE horizontal drift (HD) far detector and presents performance results using data from the ProtoDUNE-HD detector, a 770 ton liquid argon time projection chamber operated at the CERN Neutrino Platform in 2024 that served as the final prototype of the DUNE HD design. △ Less

Submitted 12 August, 2026; v1 submitted 26 April, 2026; originally announced April 2026.

Comments: Accepted version

Report number: FERMILAB-PUB-26-0270-LBNF, CERN-EP-2026-128

Journal ref: JINST 21 (2026) P08018

PipeLive: Efficient Live In-place Pipeline Parallelism Reconfiguration for Dynamic LLM Serving

Authors: Xu Bai, Muhammed Tawfiqul Islam, Chen Wang, Adel N. Toosi

Abstract: Pipeline parallelism (PP) is widely used to partition layers of large language models (LLMs) across GPUs, enabling scalable inference for large models. However, existing systems rely on static PP configurations that fail to adapt to dynamic settings, such as serverless platforms and heterogeneous GPU environments. Reconfiguring PP by stopping and redeploying service incurs prohibitive downtime, so… ▽ More Pipeline parallelism (PP) is widely used to partition layers of large language models (LLMs) across GPUs, enabling scalable inference for large models. However, existing systems rely on static PP configurations that fail to adapt to dynamic settings, such as serverless platforms and heterogeneous GPU environments. Reconfiguring PP by stopping and redeploying service incurs prohibitive downtime, so reconfiguration must instead proceed live and in place, without interrupting inference. However, live in-place PP reconfiguration is fundamentally challenging. GPUs are already saturated with model weights and KV cache, leaving little room for new layer placements and necessitating KV cache resizing, at odds with systems like vLLM that preallocate for throughput. Moreover, maintaining KV consistency during execution is difficult: stop-and-copy introduces large pauses, while background synchronization risks inconsistency as states evolve. We present PipeLive, which enables live in-place PP reconfiguration with minimal disruption. PipeLive introduces a redesigned KV cache layout together with a co-designed extension to PageAttention, forming a unified mechanism for live KV resizing. It further adopts an incremental KV patching mechanism, inspired by live virtual machine migration, to synchronize KV states between source and target configurations and identify a safe switch point. PipeLive achieves a 2.5X reduction in time-to-first-token (TTFT) without KV cache overflow compared to disabling KV resizing. Furthermore, compared to a variant without KV patching, it reduces reconfiguration overhead from seconds to under 10ms, and improves TTFT and time-per-output-token (TPOT) by up to 54.7% and 14.7%, respectively. △ Less

Submitted 13 April, 2026; originally announced April 2026.

RouterWise: Joint Resource Allocation and Routing for Latency-Aware Multi-Model LLM Serving

Authors: Hossein Hosseini Kasnavieh, Gholamreza Haffari, Christopher Leckie, Adel N. Toosi

Abstract: Multi-model LLM routing has emerged as an effective approach for reducing serving cost and latency while maintaining output quality by assigning each prompt to an appropriate model. However, prior routing methods typically assume that each model has a fixed latency. In real deployments, this assumption is inaccurate: multiple models often share limited GPU resources, and a model's latency depends… ▽ More Multi-model LLM routing has emerged as an effective approach for reducing serving cost and latency while maintaining output quality by assigning each prompt to an appropriate model. However, prior routing methods typically assume that each model has a fixed latency. In real deployments, this assumption is inaccurate: multiple models often share limited GPU resources, and a model's latency depends strongly on both its allocated resources and the request load induced by the routing policy. Consequently, routing and resource allocation are tightly coupled. In this work, we study joint resource allocation and routing for latency-aware multi-model LLM serving in GPU clusters. Given a set of deployed models and a latency service-level objective (SLO), we seek a system setup and routing policy that maximize overall output quality while satisfying the latency target. We formalize this problem as a constrained joint optimization over deployment setup and routing fractions, and propose RouterWise, which combines a dual-price formulation for score-maximizing routing with setup-specific latency models derived from system profiling. RouterWise searches over feasible system setups and, for each fixed setup, computes the best routing policy under the latency target. Our results show that even on the same GPU cluster, achievable output-quality score can vary by up to 87% across retained setups, highlighting that resource allocation is a key determinant of routing performance. △ Less

Submitted 21 June, 2026; v1 submitted 12 April, 2026; originally announced April 2026.

Core and mantle thermal evolution constraints on the onset of plate tectonics and a long-lived geodynamo

Authors: Valentin Bonnet Gibet, Nicola Tosi

Abstract: Earth's long-lived geodynamo is difficult to reconcile with recent high estimates of the core thermal conductivity, a problem known as the new core paradox. At the same time, the long-term thermal evolution of the mantle remains uncertain, largely due to the poorly constrained onset of modern-style plate tectonics, which marks the transition to efficient cooling of the interior through mobile-lid… ▽ More Earth's long-lived geodynamo is difficult to reconcile with recent high estimates of the core thermal conductivity, a problem known as the new core paradox. At the same time, the long-term thermal evolution of the mantle remains uncertain, largely due to the poorly constrained onset of modern-style plate tectonics, which marks the transition to efficient cooling of the interior through mobile-lid convection. Because core cooling -- and thus magnetic field generation -- depends on the efficiency with which the mantle extracts heat from the core, these two problems are closely linked. Here, we investigate the coupled thermal evolution of mantle and core using a 1D model that incorporates a parametrized transition transition from stagnant- to mobile-lid convection, defined by its onset time and with a prescribed duration. This framework allows us to assess how different tectonic histories influence Earth's thermal and magnetic evolution. We perform a Bayesian inversion using constraints from the palaeomagnetic record, mantle cooling history, and present-day thermal state. Our results favour a transition from stagnant- to mobile-lid convection during the Archean, which promotes core cooling and enables a geodynamo throughout Earth's history, even for core thermal conductivities in excess of 100 W/m/K. A delayed onset of mobile-lid convection provides thus a viable solution to the new core paradox. △ Less

Submitted 20 August, 2026; v1 submitted 26 March, 2026; originally announced March 2026.

Comments: 46 pages, 10 figures, - for review in jSEDI

Dynamic Menu-Based Pricing for Electric Vehicle Charging with Vehicle-to-Grid Integration

Authors: Mozhdeh Hematiboroujeni, Pierre Le Bodic, Adel N. Toosi, Markus Wagner

Abstract: The number of electric vehicles is rapidly increasing worldwide. This growth brings significant environmental benefits but also introduces new challenges: uncoordinated charging can place stress on the grid, particularly during peak hours. Beyond these challenges lies the opportunity for electric vehicles to feed energy back to the grid (V2G), which helps balance supply and demand and supports ren… ▽ More The number of electric vehicles is rapidly increasing worldwide. This growth brings significant environmental benefits but also introduces new challenges: uncoordinated charging can place stress on the grid, particularly during peak hours. Beyond these challenges lies the opportunity for electric vehicles to feed energy back to the grid (V2G), which helps balance supply and demand and supports renewable energy. However, current pricing schemes such as time-of-use tariffs provide little incentive for discharging. To study incentive design in a realistic context, we focus on a parking lot operator who manages multiple EV chargers. We propose a menu-based pricing mechanism in which each EV declares its energy requirement and parking duration; given the retail real-time electricity prices, the operator offers a menu of options that trade off the allowed level of discharging and the associated price. We formulate this interaction as a bilevel optimization problem and reformulate it into a single-level model. Results show that, relative to a no-V2G baseline, the proposed mechanism increases operator profit by 30% and reduces EV payments by 17%. Compared to widely used tariff baselines, it improves operator profit by 22-29 percent, lowers EV payments by 9-18 percent, and increases V2G contribution by 87-235 percent. Overall, the results show that the proposed dynamic menu-based pricing framework provides a practical, computationally efficient, and economically advantageous approach for real-time EV charging and V2G integration. △ Less

Submitted 5 March, 2026; originally announced March 2026.

Multi-Objective Load Balancing for Heterogeneous Edge-Based Object Detection Systems

Authors: Daghash K. Alqahtani, Maria A. Rodriguez, Muhammad Aamir Cheema, Adel N. Toosi

Abstract: The rapid proliferation of the Internet of Things (IoT) and smart applications has led to a surge in data generated by distributed sensing devices. Edge computing is a mainstream approach to managing this data by pushing computation closer to the data source, typically onto resource-constrained devices such as single-board computers (SBCs). In such environments, the unavoidable heterogeneity of ha… ▽ More The rapid proliferation of the Internet of Things (IoT) and smart applications has led to a surge in data generated by distributed sensing devices. Edge computing is a mainstream approach to managing this data by pushing computation closer to the data source, typically onto resource-constrained devices such as single-board computers (SBCs). In such environments, the unavoidable heterogeneity of hardware and software makes effective load balancing particularly challenging. In this paper, we propose a multi-objective load balancing method tailored to heterogeneous, edge-based object detection systems. We study a setting in which multiple device-model pairs expose distinct accuracy, latency, and energy profiles, while both request intensity and scene complexity fluctuate over time. To handle this dynamically varying environment, our approach uses a two-stage decision mechanism: it first performs accuracy-aware filtering to identify suitable device-model candidates that provide accuracy within the acceptable range, and then applies a weighted-sum scoring function over expected latency and energy consumption to select the final execution target. We evaluate the proposed load balancer through extensive experiments on real-world datasets, comparing against widely used baseline strategies. The results indicate that the proposed multi-objective load balancing method halves energy consumption and achieves an 80% reduction in end-to-end latency, while incurring only a modest, up to 10%, decrease in detection accuracy relative to an accuracy-centric baseline. △ Less

Submitted 16 March, 2026; originally announced March 2026.

Multi-Layer Scheduling for MoE-Based LLM Reasoning

Authors: Yifan Sun, Gholamreza Haffari, Minxian Xu, Rajkumar Buyya, Adel N. Toosi

Abstract: Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks, but serving them efficiently at scale remains a critical challenge due to their substantial computational and latency demands. While most existing inference frameworks rely on simple scheduling strategies such as First-Come-First-Serve (FCFS) at the engine level and Round-Robin (RR) at the scheduler or coor… ▽ More Large Language Models (LLMs) have achieved remarkable success across a wide range of tasks, but serving them efficiently at scale remains a critical challenge due to their substantial computational and latency demands. While most existing inference frameworks rely on simple scheduling strategies such as First-Come-First-Serve (FCFS) at the engine level and Round-Robin (RR) at the scheduler or coordinator level, they often fail to fully utilize system resources and may suffer from issues such as head-of-line blocking and load imbalance. Recent advances in Mixture-of-Experts (MoE) models have also introduced new challenges in scheduling arising from expert parallelism and routing complexity. This research proposes a multi-layer scheduling framework tailored for MoE-based LLM serving. It targets scheduling at three levels: request-level, enginelevel, and expert-level. At the request level, we explore algorithms such as Shortest-Job-First (SJF) and priority-aware aging to improve throughput and reduce latency. At the engine level, we design load-aware dispatching strategies that account for the current prefix token load, KV cache utilization, and user stickiness to achieve better resource matching. At the expert level, we focus on alleviating expert hotspots and strategically placing inter-layer expert dependencies to balance load and improve routing efficiency. Extensive experimental results from more than 100 experiments conducted under diverse workload distributions show that our approach consistently outperforms the state-of-theart inference framework vLLM, achieving up to 17.8% reduction in Time To First Token (TTFT) latency and 13.3% reduction in Time-Per-Output-Token (TPOT) latency. △ Less

Submitted 2 March, 2026; v1 submitted 25 February, 2026; originally announced February 2026.

Comments: 12 pages, 10 figures

DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting

Authors: Prabhjot Singh, Adel N. Toosi, Rajkumar Buyya

Abstract: Circuit cutting decomposes a large quantum circuit into smaller subcircuits executed independently; expectation values are recovered by classically combining subcircuit outcomes. Prior work characterises cutting overhead via subcircuit counts and sampling complexity, but its end-to-end impact on iterative, estimator-driven training pipelines remains under-measured from a systems perspective. We pr… ▽ More Circuit cutting decomposes a large quantum circuit into smaller subcircuits executed independently; expectation values are recovered by classically combining subcircuit outcomes. Prior work characterises cutting overhead via subcircuit counts and sampling complexity, but its end-to-end impact on iterative, estimator-driven training pipelines remains under-measured from a systems perspective. We propose DistributedEstimator, a cut-aware estimator execution pipeline that treats circuit cutting as a staged distributed workload, instrumenting each query across four phases: partitioning, subexperiment generation, parallel execution, and classical reconstruction. Using logged runtime traces and learning outcomes on two binary classification workloads (Iris and MNIST), we quantify cutting overheads, scaling limits, and sensitivity to injected stragglers, and assess whether accuracy and robustness are preserved under matched training budgets. Reconstruction dominates per-query time -- a median of 53% and 95th percentile of 58% at three cuts -- bounding achievable speed-up under parallelism. Despite this, test accuracy is fully preserved on Iris and maintained without systematic degradation on MNIST across all cut configurations. Robustness under Gaussian noise and FGSM perturbations is similarly preserved, with several configurations matching or improving on the uncut baseline. Exponential growth of subexperiment counts (${O}(9^c)$ for CNOT-based decomposition) is a fundamental barrier limiting practical experimentation to small qubit counts. These results establish that practical scaling for learning workloads requires reducing and overlapping reconstruction, scheduling policies for barrier-dominated critical paths, and computationally efficient reconstruction strategies for larger qubit counts. △ Less

Submitted 19 June, 2026; v1 submitted 18 February, 2026; originally announced February 2026.

LLM-Driven Intent-Based Privacy-Aware Orchestration Across the Cloud-Edge Continuum

Authors: Zijie Su, Muhammed Tawfiqul Islam, Mohammad Goudarzi, Adel N. Toosi

Abstract: With the rapid advancement of large language models (LLMs), efficiently serving LLM inference under limited GPU resources has become a critical challenge. Recently, an increasing number of studies have explored applying serverless computing paradigms to LLM serving in order to maximize resource utilization. However, LLM inference workloads are highly diverse, and modern GPU clusters are inherently… ▽ More With the rapid advancement of large language models (LLMs), efficiently serving LLM inference under limited GPU resources has become a critical challenge. Recently, an increasing number of studies have explored applying serverless computing paradigms to LLM serving in order to maximize resource utilization. However, LLM inference workloads are highly diverse, and modern GPU clusters are inherently heterogeneous, making it necessary to dynamically adjust deployment configurations online to better adapt to the elastic and dynamic nature of serverless environments. At the same time, enabling such online reconfiguration is particularly challenging due to the stateful nature of LLM inference and the massive size of model parameters. In this paper, we propose a dynamic pipeline reconfiguration approach that enables online adjustment of pipeline configurations while minimizing service downtime and performance degradation. Our method allows the system to select the optimal pipeline configuration in response to changing workloads. Experimental results on heterogeneous GPU platforms, including NVIDIA A100 and L40s, demonstrate that our migration mechanism incurs less than 50 ms of service downtime, while introducing under 10% overhead on both time-to-first-token (TTFT) and time-per-output-token (TPOT). △ Less

Submitted 17 February, 2026; originally announced February 2026.

Journal ref: 24th Australasian Symposium on Parallel and Distributed Computing (AusPDC 2026)

Service Orchestration in the Computing Continuum: Structural Challenges and Vision

Authors: Boris Sedlak, Víctor Casamayor Pujol, Ildefons Magrans de Abril, Praveen Kumar Donta, Adel N. Toosi, Schahram Dustdar

Abstract: The Computing Continuum (CC) integrates different layers of processing infrastructure, from Edge to Cloud, to optimize service quality through ubiquitous and reliable computation. Compared to central architectures, however, heterogeneous and dynamic infrastructure increases the complexity for service orchestration. To guide research, this article first summarizes structural problems of the CC, and… ▽ More The Computing Continuum (CC) integrates different layers of processing infrastructure, from Edge to Cloud, to optimize service quality through ubiquitous and reliable computation. Compared to central architectures, however, heterogeneous and dynamic infrastructure increases the complexity for service orchestration. To guide research, this article first summarizes structural problems of the CC, and then, envisions an ideal solution for autonomous service orchestration across the CC. As one instantiation, we show how Active Inference, a concept from neuroscience, can support self-organizing services in continuously interpreting their environment to optimize service quality. Still, we conclude that no existing solution achieves our vision, but that research on service orchestration faces several structural challenges. Most notably: provide standardized simulation and evaluation environments for comparing the performance of orchestration mechanisms. Together, the challenges outline a research roadmap toward resilient and scalable service orchestration in the CC. △ Less

Submitted 17 February, 2026; originally announced February 2026.

Reconstruction of atmospheric neutrinos in DUNE's horizontal-drift far-detector module

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, C. Adriano, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade, C. Andreopoulos , et al. (1325 additional authors not shown)

Abstract: This paper reports on the capabilities in reconstructing and identifying atmospheric neutrino interactions in one of the Deep Underground Neutrino Experiment's (DUNE) far detector modules, a liquid argon time projection chamber (LArTPC) with horizontal drift (FD-HD) of ionization electrons. The reconstruction is based upon the workflow developed for DUNE's long-baseline oscillation analysis, with… ▽ More This paper reports on the capabilities in reconstructing and identifying atmospheric neutrino interactions in one of the Deep Underground Neutrino Experiment's (DUNE) far detector modules, a liquid argon time projection chamber (LArTPC) with horizontal drift (FD-HD) of ionization electrons. The reconstruction is based upon the workflow developed for DUNE's long-baseline oscillation analysis, with some necessary machine-learning models' retraining and the addition of features relevant only to atmospheric neutrinos such as the neutrino direction reconstruction. Where relevant, the impact of the detection of the charged particles of the hadronic system is emphasized, and comparisons are carried out between the case when lepton-only information is considered in the reconstruction (as is the case for many neutrino oscillation experiments), versus when all particles identified in the LArTPC were included. Three neutrino direction reconstruction methods have been developed and studied for the atmospheric analyses: using lepton-only information, using all reconstructed particles, and using only correlations from reconstructed hits. The results indicate that incorporating more than just lepton information significantly improves the resolution of both neutrino direction and energy reconstruction. The angle reconstruction algorithms developed in this work result in no strong dependence on particle direction for reconstruction efficiencies or neutrino flavor identification. This comprehensive review of the reconstruction of atmospheric neutrinos in DUNE's FD-HD LArTPC is the first step towards developing a first neutrino oscillation sensitivity analysis, which will ready DUNE for its first measurements. △ Less

Submitted 9 January, 2026; originally announced January 2026.

Comments: 43 pages, 23 figures

Report number: FERMILAB-PUB-25-0961-LBNF

IntroLM: Introspective Language Models via Prefilling-Time Self-Evaluation

Authors: Hossein Hosseini Kasnavieh, Gholamreza Haffari, Chris Leckie, Adel N. Toosi

Abstract: A major challenge for the operation of large language models (LLMs) is how to predict whether a specific LLM will produce sufficiently high-quality output for a given query. Existing approaches rely on external classifiers, most commonly BERT based models, which suffer from limited context windows, constrained representational capacity, and additional computational overhead. We propose IntroLM, a… ▽ More A major challenge for the operation of large language models (LLMs) is how to predict whether a specific LLM will produce sufficiently high-quality output for a given query. Existing approaches rely on external classifiers, most commonly BERT based models, which suffer from limited context windows, constrained representational capacity, and additional computational overhead. We propose IntroLM, a method that enables causal language models to predict their own output quality during the prefilling phase without affecting generation using introspective tokens. By introducing token conditional LoRA that activates only for the introspective token, the model learns to predict the output quality for a given query while preserving the original backbone behavior and avoiding external evaluators. On question answering benchmarks, IntroLM applied to Qwen3 8B achieves a ROC AUC of 90 precent for success prediction, outperforming a DeBERTa classifier by 14 precent. When integrated into multi model routing systems, IntroLM achieves superior cost performance tradeoffs, reducing latency by up to 33 precent and large model usage by up to 50 precent at matched reliability. △ Less

Submitted 8 May, 2026; v1 submitted 6 January, 2026; originally announced January 2026.

Comments: Accepted for publication in Findings of ACL 2026

Cryogenic characterization of FBK NUV-HD-Cryo 3T SiPM sensors for the DUNE photon detection system

Authors: F. Acerbi, M. Andreotti, A. Balboni, E. Bertolini, S. Bertolucci, G. Botogoske, F. Bramati, A. Branca, C. Brizzolari, G. Brunetti, R. Calabrese, E. Calvo, N. Canci, P. Carniti, D. Casazza, C. Cattadori, A. Cervera, F. Chiapponi, S. Chiozzi, V. Cicero, A. Cotta Ramusino, E. Cristaldo Morales, C. Cuesta, R. D Amico, L. Degli Esposti , et al. (71 additional authors not shown)

Abstract: The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino experiment based in the USA and composed of a Near Detector (ND) complex at Fermi National Laboratory (FNAL), and a Far Detector (FD) complex located at the Sanford Underground Research Facility (SURF) $\sim$1300\,km distant. DUNE will study neutrino oscillations looking for unresolved issues of the Standard Model of parti… ▽ More The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino experiment based in the USA and composed of a Near Detector (ND) complex at Fermi National Laboratory (FNAL), and a Far Detector (FD) complex located at the Sanford Underground Research Facility (SURF) $\sim$1300\,km distant. DUNE will study neutrino oscillations looking for unresolved issues of the Standard Model of particle physics (SM) such as CP violation in the leptonic sector, neutrino mass ordering and others, starting from the early 2030s. The FD, with a mass of $\sim$17\,kt, that will exploit both ionization and scintillation signals to detect neutrino interactions with Argon. Scintillating photons in LAr will be detected by the photon detection system (PDS) based on light collectors coupled to Silicon Photomultipliers (SiPMs). During a test campaign, different laboratories of the collaboration performed an investigation of the best SiPM candidates that fulfill the DUNE FD requirements. We identified two models of SiPM, produced by Hamamatsu Photonics K.K. (HPK) and Fondazione Bruno Kessler (FBK), respectively. In this paper, we focus on the FBK selected model showing its main features. We will describe the characterization protocol, the results at both room and cryogenic temperatures and the photon detection efficiency measurements. △ Less

Submitted 24 November, 2025; originally announced November 2025.

Measurement of Exclusive $π^+$--argon Interactions Using ProtoDUNE-SP

Authors: DUNE Collaboration, S. Abbaslu, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, C. Adriano, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade, C. Andreopoulos, M. Andreotti , et al. (1304 additional authors not shown)

Abstract: We present the measurement of $π^{+}$--argon inelastic cross sections using the ProtoDUNE Single-Phase liquid argon time projection chamber in the incident $π^+$ kinetic energy range of 500 -- 800 MeV in multiple exclusive channels (absorption, charge exchange, and the remaining inelastic interactions). The results of this analysis are important inputs to simulations of liquid argon neutrino exper… ▽ More We present the measurement of $π^{+}$--argon inelastic cross sections using the ProtoDUNE Single-Phase liquid argon time projection chamber in the incident $π^+$ kinetic energy range of 500 -- 800 MeV in multiple exclusive channels (absorption, charge exchange, and the remaining inelastic interactions). The results of this analysis are important inputs to simulations of liquid argon neutrino experiments such as the Deep Underground Neutrino Experiment and the Short Baseline Neutrino program at Fermi National Accelerator Laboratory. They will be employed to improve the modeling of final state interactions within neutrino event generators used by these experiments, as well as the modeling of $π^{+}$--argon secondary interactions within the liquid argon. This is the first measurement of $π^+$--argon absorption at this kinetic energy range as well as the first ever measurement of $π^{+}$--argon charge exchange. △ Less

Submitted 17 November, 2025; originally announced November 2025.

Report number: CERN-EP-2025-268; FERMILAB-PUB-25-0732-LBNF

First Measurement of $π^+$-Ar and $p$-Ar Total Inelastic Cross Sections in the Sub-GeV Energy Regime with ProtoDUNE-SP Data

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, C. Adriano, F. Akbar, F. Alemanno, N. S. Alex, L. Aliaga Soplin, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade , et al. (1328 additional authors not shown)

Abstract: The ProtoDUNE-SP detector, a kiloton-scale prototype for the Deep Underground Neutrino Experiment (DUNE) far detector, is the largest liquid argon time projection chamber built to date. Operated at CERN from 2018 to 2020, it collected both cosmic-ray data and a beam consisting of positively-charged particles with discrete momentum settings across a range of 0.3 GeV/$c$ to 7 GeV/$c$. In this letter… ▽ More The ProtoDUNE-SP detector, a kiloton-scale prototype for the Deep Underground Neutrino Experiment (DUNE) far detector, is the largest liquid argon time projection chamber built to date. Operated at CERN from 2018 to 2020, it collected both cosmic-ray data and a beam consisting of positively-charged particles with discrete momentum settings across a range of 0.3 GeV/$c$ to 7 GeV/$c$. In this letter, we report the total inelastic cross section measurements for $π^+$--Ar and $p$--Ar interactions using selected $π^+$ and proton samples from the 1 GeV/$c$ beam data, spanning kinetic energies of 500--900~MeV and below 450~MeV, respectively. These energy ranges are directly relevant to hadrons produced in DUNE. The measured cross sections are consistent with predictions and provide a dataset that was previously unavailable for argon targets. These measurements are essential for constraining neutrino-argon interaction models and achieving the precision physics goals of the upcoming DUNE experiment. △ Less

Submitted 26 May, 2026; v1 submitted 14 November, 2025; originally announced November 2025.

Report number: FERMILAB-PUB-25-0814-LBNF, CERN-EP-2025-266

Identification of low-energy kaons in the ProtoDUNE-SP detector

Authors: DUNE Collaboration, S. Abbaslu, F. Abd Alrahman, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, C. Adriano, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade, C. Andreopoulos , et al. (1325 additional authors not shown)

Abstract: The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment with a rich physics program that includes searches for the hypothetical phenomenon of proton decay. Utilizing liquid-argon time-projection chamber technology, DUNE is expected to achieve world-leading sensitivity in the proton decay channels that involve charged kaons in their final states. The first DUNE demo… ▽ More The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment with a rich physics program that includes searches for the hypothetical phenomenon of proton decay. Utilizing liquid-argon time-projection chamber technology, DUNE is expected to achieve world-leading sensitivity in the proton decay channels that involve charged kaons in their final states. The first DUNE demonstrator, ProtoDUNE Single-Phase, was a 0.77 kt detector that operated from 2018 to 2020 at the CERN Neutrino Platform, exposed to a mixed hadron and electron test-beam with momenta ranging from 0.3 to 7 GeV/c. We present a selection of low-energy kaons among the secondary particles produced in hadronic reactions, using data from the 6 and 7 GeV/c beam runs. The selection efficiency is 1\% and the sample purity 92\%. The initial energies of the selected kaon candidates encompass the expected energy range of kaons originating from proton decay events in DUNE (below $\sim$200 MeV). In addition, we demonstrate the capability of this detector technology to discriminate between kaons and other particles such as protons and muons, and provide a comprehensive description of their energy loss in liquid argon, which shows good agreement with the simulation. These results pave the way for future proton decay searches at DUNE. △ Less

Submitted 9 October, 2025; originally announced October 2025.

Report number: CERN-EP-2025-231, FERMILAB-PUB-25-0717-LBNF

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum

Authors: Xu Bai, Muhammed Tawfiqul Islam, Rajkumar Buyya, Adel N. Toosi

Abstract: Cloud computing, despite its advantages in scalability, may not always fully satisfy the low-latency demands of emerging latency-sensitive pervasive applications. The cloud-edge continuum addresses this by integrating the responsiveness of edge resources with cloud scalability. Microservice Architecture (MSA) characterized by modular, loosely coupled services, aligns effectively with this continuu… ▽ More Cloud computing, despite its advantages in scalability, may not always fully satisfy the low-latency demands of emerging latency-sensitive pervasive applications. The cloud-edge continuum addresses this by integrating the responsiveness of edge resources with cloud scalability. Microservice Architecture (MSA) characterized by modular, loosely coupled services, aligns effectively with this continuum. However, the heterogeneous and dynamic computing resource poses significant challenges to the optimal placement of microservices. We propose REACH, a novel rescheduling algorithm that dynamically adapts microservice placement in real time using reinforcement learning to react to fluctuating resource availability, and performance variations across distributed infrastructures. Extensive experiments on a real-world testbed demonstrate that REACH reduces average end-to-end latency by 7.9%, 10%, and 8% across three benchmark MSA applications, while effectively mitigating latency fluctuations and spikes. △ Less

Submitted 8 October, 2025; originally announced October 2025.

Comments: 10 pages, 10 figures

Towards mono-energetic virtual $ν$ beam cross-section measurements: A feasibility study of $ν$-Ar interaction analysis with DUNE-PRISM

Authors: DUNE Collaboration, S. Abbaslu, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, C. Adriano, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade, C. Andreopoulos, M. Andreotti , et al. (1302 additional authors not shown)

Abstract: Neutrino-nucleus cross-section measurements are critical for future neutrino oscillation analyses. However, our models to describe them require further refinement, and a deeper understanding of the underlying physics is essential for future neutrino oscillation experiments to realize their ambitious physics goals. Current neutrino cross-section measurements provide clear deficiencies in neutrino i… ▽ More Neutrino-nucleus cross-section measurements are critical for future neutrino oscillation analyses. However, our models to describe them require further refinement, and a deeper understanding of the underlying physics is essential for future neutrino oscillation experiments to realize their ambitious physics goals. Current neutrino cross-section measurements provide clear deficiencies in neutrino interaction modeling, but almost all are reported averaged over broad neutrino fluxes, rendering their interpretation challenging. Using the DUNE-PRISM concept (Deep Underground Neutrino Experiment Precision Reaction Independent Spectrum Measurement) -- a movable near detector that samples multiple off-axis positions -- neutrino interaction measurements can be used to construct narrow virtual fluxes (less than 100 MeV wide). These fluxes can be used to extract charged-current neutrino-nucleus cross sections as functions of outgoing lepton kinematics within specific neutrino energy ranges. Based on a dedicated simulation with realistic event statistics and flux-related systematic uncertainties, but assuming an almost-perfect detector, we run a feasibility study demonstrating how DUNE-PRISM data can be used to measure muon neutrino charged-current integrated and differential cross sections over narrow fluxes. We find that this approach enables a model independent reconstruction of powerful observables, including energy transfer, typically accessible only in electron scattering measurements, but that large exposures may be required for differential cross-section measurements with few-\% statistical uncertainties. △ Less

Submitted 9 September, 2025; originally announced September 2025.

Report number: FERMILAB-PUB-25-0627-LBNF

Journal ref: Phys. Rev. D 114, 012018 (2026)

Operation of a Modular 3D-Pixelated Liquid Argon Time-Projection Chamber in a Neutrino Beam

Authors: DUNE Collaboration, S. Abbaslu, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, C. Adriano, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade, C. Andreopoulos, M. Andreotti , et al. (1299 additional authors not shown)

Abstract: The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) neutrino beam at Fermi National Accelerator Laboratory (Fermilab). This detector prototypes a new modular design for a liquid argon time-projection chamber (LArTPC), comprised of a two-by-two array of four modules, each f… ▽ More The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) neutrino beam at Fermi National Accelerator Laboratory (Fermilab). This detector prototypes a new modular design for a liquid argon time-projection chamber (LArTPC), comprised of a two-by-two array of four modules, each further segmented into two optically-isolated LArTPCs. The 2x2 Demonstrator features a number of pioneering technologies, including a low-profile resistive field shell to establish drift fields, native 3D ionization pixelated imaging, and a high-coverage dielectric light readout system. The 2.4 tonne active mass detector is flanked upstream and downstream by supplemental solid-scintillator tracking planes, repurposed from the MINERvA experiment, which track ionizing particles exiting the argon volume. The antineutrino beam data collected by the detector over a 4.5 day period in 2024 include over 30,000 neutrino interactions in the LAr active volume-the first neutrino interactions reported by a DUNE detector prototype. During its physics-quality run, the 2x2 Demonstrator operated at a nominal drift field of 500 V/cm and maintained good LAr purity, with a stable electron lifetime of approximately 1.25 ms. This paper describes the detector and supporting systems, summarizes the installation and commissioning, and presents the initial validation of collected NuMI beam and off-beam self-triggers. In addition, it highlights observed interactions in the detector volume, including candidate muon anti-neutrino events. △ Less

Submitted 17 June, 2026; v1 submitted 6 September, 2025; originally announced September 2025.

Report number: FERMILAB-PUB-25-0537-LBNF

Journal ref: Instruments 2026, 10(1), 18

Resilience Evaluation of Kubernetes in Cloud-Edge Environments via Failure Injection

Authors: Zihao Chen, Mohammad Goudarzi, Adel Nadjaran Toosi

Abstract: Kubernetes has emerged as an essential platform for deploying containerised applications across cloud and edge infrastructures. As Kubernetes gains increasing adoption for mission-critical microservices, evaluating system resilience under realistic fault conditions becomes crucial. However, systematic resilience assessments of Kubernetes in hybrid cloud-edge environments are currently limited in r… ▽ More Kubernetes has emerged as an essential platform for deploying containerised applications across cloud and edge infrastructures. As Kubernetes gains increasing adoption for mission-critical microservices, evaluating system resilience under realistic fault conditions becomes crucial. However, systematic resilience assessments of Kubernetes in hybrid cloud-edge environments are currently limited in research. To address this gap, a novel resilience evaluation framework integrates mainstream fault injection tools with automated workload generation for comprehensive cloud-edge Kubernetes testing. Multiple fault injection platforms, including Chaos Mesh, Gremlin, and ChaosBlade are combined with realistic traffic simulation tools, enabling automated orchestration of complex failure scenarios. Through this framework, comprehensive experiments are conducted that systematically target node-level, pod-level, and network failures across cloud and cloud-edge environments. The first comprehensive resilience dataset for hybrid cloud-edge Kubernetes deployments is created, comprising over 30 GB of performance data from 11,965 fault injection scenarios including response times, failure rates, and error patterns. Analysis reveals that cloud-edge deployments demonstrate 80% superior response stability under network delay and partition conditions, while cloud deployments exhibit 47% better resilience under bandwidth limitations, providing quantitative guidance for architectural decision-making in cloud-edge deployments. △ Less

Submitted 21 July, 2025; originally announced July 2025.

Efficient Routing of Inference Requests across LLM Instances in Cloud-Edge Computing

Authors: Shibo Yu, Mohammad Goudarzi, Adel Nadjaran Toosi

Abstract: The rising demand for Large Language Model (LLM) inference services has intensified pressure on computational resources, resulting in latency and cost challenges. This paper introduces a novel routing algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute inference requests across heterogeneous LLM instances in a cloud-edge computing environment. Formulated as a… ▽ More The rising demand for Large Language Model (LLM) inference services has intensified pressure on computational resources, resulting in latency and cost challenges. This paper introduces a novel routing algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute inference requests across heterogeneous LLM instances in a cloud-edge computing environment. Formulated as a multi-objective optimization problem, the algorithm balances response quality, response time, and inference cost, adapting to request heterogeneity (e.g., varying complexity and prompt lengths) and node diversity (e.g., edge vs. cloud resources). This adaptive routing algorithm optimizes performance under dynamic workloads. We benchmark the approach using a testbed with datasets including Stanford Question Answering Dataset (SQuAD), Mostly Basic Python Problems (MBPP), Hella Situations With Adversarial Generations (HellaSwag), and Grade School Math 8K (GSM8K). Experimental results show our solution, compared to the baselines, preserves 95.2% of Cloud-Only response quality with slight latency increase, while reducing inference cost by 34.9%. These findings validate the algorithm's effectiveness for scalable LLM deployments. △ Less

Submitted 14 January, 2026; v1 submitted 21 July, 2025; originally announced July 2025.

A Multi-Armed Bandit-Based Participant Selection Method for Federated Recommendation Systems

Authors: Jintao Liu, Mohammad Goudarzi, Adel Nadjaran Toosi

Abstract: Federated Recommendation Systems (FRS) enable privacy-preserving model training by keeping user data on edge devices. However, the practical deployment of FRS in Edge-Cloud environments faces significant challenges due to system and statistical heterogeneity. Existing FRS participant selection strategies struggle to dynamically balance the trade-off between model convergence speed and recommendati… ▽ More Federated Recommendation Systems (FRS) enable privacy-preserving model training by keeping user data on edge devices. However, the practical deployment of FRS in Edge-Cloud environments faces significant challenges due to system and statistical heterogeneity. Existing FRS participant selection strategies struggle to dynamically balance the trade-off between model convergence speed and recommendation quality in such volatile environments. To address this, we formulate the FRS participant selection problem as a normalized utility cost addressing the model quality and system efficiency. Next, we propose a dynamic participant selection framework incorporating a Multi-Armed Bandit (MAB)-based solver for multimodal FRS. We design a client-utility function that jointly evaluates historical Client Performance Reputation, data quality, and real-time system latency. By leveraging an Upper Confidence Bound strategy, our framework effectively balances the exploration of under-sampled clients with the exploitation of high-performing ones. We validate the proposed approach on a realistic edge-cloud testbed implementation using a multimodal movie-recommendation task. Experimental results demonstrate that our MAB-driven approach outperforms other baselines across eight different data-skew scenarios. Specifically, it improves training efficiency by 32-50% while improving model quality metrics such as Recall@50 by up to around 5% △ Less

Submitted 24 May, 2026; v1 submitted 21 July, 2025; originally announced July 2025.

Comments: Accepted in IEEE/ACM CCGRID 2026

Impact Analysis of Optimal EV Bi-directional Charging with Spatial-temporal Constraints

Authors: Xian-Long Lee, Adel N. Toosi, Peter Pudney, Ian McLeod, Muhammad Aamir Cheema, Hao Wang

Abstract: The growth in Electric Vehicle (EV) market share is expected to increase power demand on distribution networks. Uncoordinated residential EV charging, based on driving routines, creates peak demand at various zone substations depending on location and time. Leveraging smart charge scheduling and Vehicle-to-Grid (V2G) technologies offers opportunities to adjust charge schedules, allowing for load s… ▽ More The growth in Electric Vehicle (EV) market share is expected to increase power demand on distribution networks. Uncoordinated residential EV charging, based on driving routines, creates peak demand at various zone substations depending on location and time. Leveraging smart charge scheduling and Vehicle-to-Grid (V2G) technologies offers opportunities to adjust charge schedules, allowing for load shifting and grid support, which can reduce both charging costs and grid stress. In this work, we develop a charge scheduling optimization method that can be used to assess the impact of spatial power capacity constraints and real-time price profiles. We formulate a mixed-integer linear programming problem to minimize overall charging costs, taking into account factors such as time-varying EV locations, EV charging requirements, and local power demands across different zones. Our analysis uses real data for pricing signals and local power demands, combined with simulated data for EV driving plans. Four metrics are introduced to assess impacts from the perspectives of both EV users and zones. Results indicate that overall EV charging costs are only minimally affected under extreme power capacity constraints. △ Less

Submitted 17 July, 2025; originally announced July 2025.

Spatial and Temporal Evaluations of the Liquid Argon Purity in ProtoDUNE-SP

Authors: DUNE Collaboration, S. Abbaslu, A. Abed Abud, R. Acciarri, L. P. Accorsi, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, C. Adriano, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade, C. Andreopoulos, M. Andreotti , et al. (1301 additional authors not shown)

Abstract: Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-Phase (ProtoDUNE-SP) was an approximately 700-ton liquid argon detector intended to prototype the Deep Underground Neutrino Experiment (DUNE) Far Detector Horizontal Drift module. It contains two drift volumes bisected by… ▽ More Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-Phase (ProtoDUNE-SP) was an approximately 700-ton liquid argon detector intended to prototype the Deep Underground Neutrino Experiment (DUNE) Far Detector Horizontal Drift module. It contains two drift volumes bisected by the cathode plane assembly, which is biased to create an almost uniform electric field in both volumes. The DUNE Far Detector modules must have robust cryogenic systems capable of filtering argon and supplying the TPC with clean liquid. This paper will explore comparisons of the argon purity measured by the purity monitors with those measured using muons in the TPC from October 2018 to November 2018. A new method is introduced to measure the liquid argon purity in the TPC using muons crossing both drift volumes of ProtoDUNE-SP. For extended periods on the timescale of weeks, the drift electron lifetime was measured to be above 30 ms using both systems. A particular focus will be placed on the measured purity of argon as a function of position in the detector. △ Less

Submitted 27 August, 2025; v1 submitted 11 July, 2025; originally announced July 2025.

Report number: CERN-EP-2025-157, FERMILAB-PUB-25-0445-V

Journal ref: JINST (2025) 20 P09008

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge

Authors: Daghash K. Alqahtani, Maria A. Rodriguez, Muhammad Aamir Cheema, Hamid Rezatofighi, Adel N. Toosi

Abstract: Edge computing enables data processing closer to the source, significantly reducing latency, an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource-constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To ad… ▽ More Edge computing enables data processing closer to the source, significantly reducing latency, an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource-constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strategies, including a novel estimation-based techniques and an innovative greedy selection algorithm, to direct image processing requests to the most suitable edge device-model pair. ECORE dynamically balances energy efficiency and detection performance based on object characteristics. We evaluate our framework through extensive experiments on real-world datasets, comparing against widely used baseline techniques. The evaluation leverages established object detection models (YOLO, SSD, EfficientDet) and diverse edge platforms, including Jetson Orin Nano, Raspberry Pi 4 and 5, and TPU accelerators. Results demonstrate that our proposed context-aware routing strategies can reduce energy consumption and latency by 35% and 49%, respectively, while incurring only a 2% loss in detection accuracy compared to accuracy-centric methods. △ Less

Submitted 3 February, 2026; v1 submitted 8 July, 2025; originally announced July 2025.

Smart Ride and Delivery Services with Electric Vehicles: Leveraging Bidirectional Charging for Profit Optimisation

Authors: Jinchun Du, Bojie Shen, Muhammad Aamir Cheema, Adel N. Toosi

Abstract: With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology - allowing EVs to also d… ▽ More With the rising popularity of electric vehicles (EVs), modern service systems, such as ride-hailing delivery services, are increasingly integrating EVs into their operations. Unlike conventional vehicles, EVs often have a shorter driving range, necessitating careful consideration of charging when fulfilling requests. With recent advances in Vehicle-to-Grid (V2G) technology - allowing EVs to also discharge energy back to the grid - new opportunities and complexities emerge. We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G): a profit-maximization problem where EV drivers must select customer requests or orders while managing when and where to charge or discharge. This involves navigating dynamic electricity prices, charging station selection, and route constraints. We formulate the problem as a Mixed Integer Programming (MIP) model and propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighborhood search (LNS). Experiments on real-world data show our methods can double driver profits compared to baselines, while maintaining near-optimal performance on small instances and excellent scalability on larger ones. Our work highlights a promising path toward smarter, more profitable EV-based mobility systems that actively support the energy grid. △ Less

Submitted 26 June, 2025; v1 submitted 25 June, 2025; originally announced June 2025.

The influence of interior structure and thermal state on impact melt generation upon large impacts onto terrestrial planets

Authors: Lukas Manske, Thomas Ruedas, Ana-Catalina Plesa, Philipp Baumeister, Nicola Tosi, Natalia Artemieva, Kai Wünnemann

Abstract: We investigate the melt production of planetary impacts as a function of planet size ($R/R_\mathrm{Earth}$=0.1-1.5), impactor size ($L$=1-1000 km), and core size ratio ($R_\mathrm{core}/R$=0.2-0.8) using a combination of parameterized convection models and fully dynamical 2D impact simulations. To this end, we introduce a new method to determine impact-induced melt volumes which we normalize by th… ▽ More We investigate the melt production of planetary impacts as a function of planet size ($R/R_\mathrm{Earth}$=0.1-1.5), impactor size ($L$=1-1000 km), and core size ratio ($R_\mathrm{core}/R$=0.2-0.8) using a combination of parameterized convection models and fully dynamical 2D impact simulations. To this end, we introduce a new method to determine impact-induced melt volumes which we normalize by the impactor volume for better comparability. We find that this normalized melt production, or melting efficiency, is enhanced for large planets when struck by smaller impactors, while for small planets, melting efficiency is elevated when impacted by larger impactors. This diverging behavior can be explained by the thickness of the planets' thermal boundary layer and the shapes of their thermal and lithostatic pressure profiles. We also find that melting efficiency maxima are usually highest on Earth-size planets. We show that the melting efficiency is only affected by core size ratio for large cores and older planets, where melt production is decreased significantly compared to smaller core size ratios. Projecting the lunar impactor flux on the generic planets, we find that Moon-sized planets produce the most melt throughout their evolution, relative to planet volume. Contrary to previous scaling laws, our method accounts for melt production by decompression or plastic work in addition to shock melting. We find that traditional scaling laws underestimate melt production on length scales where variations in the target planets' lithology, temperature, and lithostatic pressure become significant. We propose empirical formulas to predict melt generation as a function of radial structure and thermal age. △ Less

Submitted 23 June, 2025; originally announced June 2025.

Comments: 32 pages, 14 figures

Report number: TRR 170 contribution 225

Journal ref: J. Geophys. Res., 130 (2025)

Physics-based machine learning for mantle convection simulations

Authors: Siddhant Agarwal, Ali Can Bekar, Christian Hüttig, David S. Greenberg, Nicola Tosi

Abstract: Mantle convection simulations are an essential tool for understanding how rocky planets evolve. However, the poorly known input parameters to these simulations, the non-linear dependence of transport properties on pressure and temperature, and the long integration times in excess of several billion years all pose a computational challenge for numerical solvers. We propose a physics-based machine l… ▽ More Mantle convection simulations are an essential tool for understanding how rocky planets evolve. However, the poorly known input parameters to these simulations, the non-linear dependence of transport properties on pressure and temperature, and the long integration times in excess of several billion years all pose a computational challenge for numerical solvers. We propose a physics-based machine learning approach that predicts creeping flow velocities as a function of temperature while conserving mass, thereby bypassing the numerical solution of the Stokes problem. A finite-volume solver then uses the predicted velocities to advect and diffuse the temperature field to the next time-step, enabling autoregressive rollout at inference. For training, our model requires temperature-velocity snapshots from a handful of simulations (94). We consider mantle convection in a two-dimensional rectangular box with basal and internal heating, pressure- and temperature-dependent viscosity. Overall, our model is up to 89 times faster than the numerical solver. We also show the importance of different components in our convolutional neural network architecture such as mass conservation, learned paddings on the boundaries, and loss scaling for the overall rollout performance. Finally, we test our approach on unseen scenarios to demonstrate some of its strengths and weaknesses. △ Less

Submitted 21 May, 2025; originally announced May 2025.

Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data

Authors: Seunghyun Lee, Omid Tavallaie, Shuaijun Chen, Kanchana Thilakarathna, Suranga Seneviratne, Adel Nadjaran Toosi, Albert Y. Zomaya

Abstract: Accommodating edge networks between IoT devices and the cloud server in Hierarchical Federated Learning (HFL) enhances communication efficiency without compromising data privacy. However, devices connected to the same edge often share geographic or contextual similarities, leading to varying edge-level data heterogeneity with different subsets of labels per edge, on top of device-level heterogenei… ▽ More Accommodating edge networks between IoT devices and the cloud server in Hierarchical Federated Learning (HFL) enhances communication efficiency without compromising data privacy. However, devices connected to the same edge often share geographic or contextual similarities, leading to varying edge-level data heterogeneity with different subsets of labels per edge, on top of device-level heterogeneity. This hierarchical non-Independent and Identically Distributed (non-IID) nature, which implies that each edge has its own optimization goal, has been overlooked in HFL research. Therefore, existing edge-accommodated HFL demonstrates inconsistent performance across edges in various hierarchical non-IID scenarios. To ensure robust performance with diverse edge-level non-IID data, we propose a Personalized Hierarchical Edge-enabled Federated Learning (PHE-FL), which personalizes each edge model to perform well on the unique class distributions specific to each edge. We evaluated PHE-FL across 4 scenarios with varying levels of edge-level non-IIDness, with extreme IoT device level non-IIDness. To accurately assess the effectiveness of our personalization approach, we deployed test sets on each edge server instead of the cloud server, and used both balanced and imbalanced test sets. Extensive experiments show that PHE-FL achieves up to 83 percent higher accuracy compared to existing federated learning approaches that incorporate edge networks, given the same number of training rounds. Moreover, PHE-FL exhibits improved stability, as evidenced by reduced accuracy fluctuations relative to the state-of-the-art FedAvg with two-level (edge and cloud) aggregation. △ Less

Submitted 11 April, 2025; originally announced April 2025.

IntentContinuum: Using LLMs to Support Intent-Based Computing Across the Compute Continuum

Authors: Negin Akbari, John Grundy, Aamir Cheema, Adel N. Toosi

Abstract: The increasing proliferation of IoT devices and AI applications has created a demand for scalable and efficient computing solutions, particularly for applications requiring real-time processing. The compute continuum integrates edge and cloud resources to meet this need, balancing the low-latency demands of the edge with the high computational power of the cloud. However, managing resources in suc… ▽ More The increasing proliferation of IoT devices and AI applications has created a demand for scalable and efficient computing solutions, particularly for applications requiring real-time processing. The compute continuum integrates edge and cloud resources to meet this need, balancing the low-latency demands of the edge with the high computational power of the cloud. However, managing resources in such a distributed environment presents challenges due to the diversity and complexity of these systems. Traditional resource management methods, often relying on heuristic algorithms, struggle to manage the increasing complexity, scale, and dynamics of these systems, as well as adapt to dynamic workloads and changing network conditions. Moreover, designing such approaches is often time-intensive and highly tailored to specific applications, demanding deep expertise. In this paper, we introduce a novel framework for intent-driven resource management in the compute continuum, using large language models (LLMs) to help automate decision-making processes. Our framework ensures that user-defined intents -- such as achieving the required response times for time-critical applications -- are consistently fulfilled. In the event of an intent violation, our system performs root cause analysis by examining system data to identify and address issues. This approach reduces the need for human intervention and enhances system reliability, offering a more dynamic and efficient solution for resource management in distributed environments. △ Less

Submitted 6 April, 2025; originally announced April 2025.

Comments: 11 pages, 10 figures

Journal ref: 2025 IEEE International Conference on Web Services (ICWS)

European Contributions to Fermilab Accelerator Upgrades and Facilities for the DUNE Experiment

Authors: DUNE Collaboration, A. Abed Abud, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade , et al. (1322 additional authors not shown)

Abstract: The Proton Improvement Plan (PIP-II) to the FNAL accelerator chain and the Long-Baseline Neutrino Facility (LBNF) will provide the world's most intense neutrino beam to the Deep Underground Neutrino Experiment (DUNE) enabling a wide-ranging physics program. This document outlines the significant contributions made by European national laboratories and institutes towards realizing the first phase o… ▽ More The Proton Improvement Plan (PIP-II) to the FNAL accelerator chain and the Long-Baseline Neutrino Facility (LBNF) will provide the world's most intense neutrino beam to the Deep Underground Neutrino Experiment (DUNE) enabling a wide-ranging physics program. This document outlines the significant contributions made by European national laboratories and institutes towards realizing the first phase of the project with a 1.2 MW neutrino beam. Construction of this first phase is well underway. For DUNE Phase II, this will be closely followed by an upgrade of the beam power to > 2 MW, for which the European groups again have a key role and which will require the continued support of the European community for machine aspects of neutrino physics. Beyond the neutrino beam aspects, LBNF is also responsible for providing unique infrastructure to install and operate the DUNE neutrino detectors at FNAL and at the Sanford Underground Research Facility (SURF). The cryostats for the first two Liquid Argon Time Projection Chamber detector modules at SURF, a contribution of CERN to LBNF, are central to the success of the ongoing execution of DUNE Phase I. Likewise, successful and timely procurement of cryostats for two additional detector modules at SURF will be critical to the success of DUNE Phase II and the overall physics program. The DUNE Collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This paper is being submitted to the 'Accelerator technologies' and 'Projects and Large Experiments' streams. Additional inputs related to the DUNE science program, DUNE detector technologies and R&D, and DUNE software and computing, are also being submitted to other streams. △ Less

Submitted 31 March, 2025; originally announced March 2025.

Comments: Submitted to the 2026 Update of the European Strategy for Particle Physics

DUNE Software and Computing Research and Development

Authors: DUNE Collaboration, A. Abed Abud, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade , et al. (1322 additional authors not shown)

Abstract: The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The ambitious physics program of Phase I and Phase II of DUNE is dependent upon deployment and utilization of significant computing res… ▽ More The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The ambitious physics program of Phase I and Phase II of DUNE is dependent upon deployment and utilization of significant computing resources, and successful research and development of software (both infrastructure and algorithmic) in order to achieve these scientific goals. This submission discusses the computing resources projections, infrastructure support, and software development needed for DUNE during the coming decades as an input to the European Strategy for Particle Physics Update for 2026. The DUNE collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This submission to the 'Computing' stream focuses on DUNE software and computing. Additional inputs related to the DUNE science program, DUNE detector technologies and R&D, and European contributions to Fermilab accelerator upgrades and facilities for the DUNE experiment, are also being submitted to other streams. △ Less

Submitted 31 March, 2025; originally announced March 2025.

Comments: Submitted to the 2026 Update of the European Strategy for Particle Physics

The DUNE Phase II Detectors

Authors: DUNE Collaboration, A. Abed Abud, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade , et al. (1322 additional authors not shown)

Abstract: The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy for the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and… ▽ More The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy for the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the previous European Strategy for Particle Physics. The construction of DUNE Phase I is well underway. DUNE Phase II consists of a third and fourth far detector module, an upgraded near detector complex, and an enhanced > 2 MW beam. The fourth FD module is conceived as a 'Module of Opportunity', aimed at supporting the core DUNE science program while also expanding the physics opportunities with more advanced technologies. The DUNE collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This submission to the 'Detector instrumentation' stream focuses on technologies and R&D for the DUNE Phase II detectors. Additional inputs related to the DUNE science program, DUNE software and computing, and European contributions to Fermilab accelerator upgrades and facilities for the DUNE experiment, are also being submitted to other streams. △ Less

Submitted 29 March, 2025; originally announced March 2025.

Comments: Submitted to the 2026 Update of the European Strategy for Particle Physics

The DUNE Science Program

Authors: DUNE Collaboration, A. Abed Abud, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, D. A. Andrade , et al. (1322 additional authors not shown)

Abstract: The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy for the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and… ▽ More The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy for the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the previous European Strategy for Particle Physics. The construction of DUNE Phase I is well underway. DUNE Phase II consists of a third and fourth far detector module, an upgraded near detector complex, and an enhanced > 2 MW beam. The fourth FD module is conceived as a 'Module of Opportunity', aimed at supporting the core DUNE science program while also expanding the physics opportunities with more advanced technologies. The DUNE collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This submission to the 'Neutrinos and cosmic messengers', 'BSM physics' and 'Dark matter and dark sector' streams focuses on the physics program of DUNE. Additional inputs related to DUNE detector technologies and R&D, DUNE software and computing, and European contributions to Fermilab accelerator upgrades and facilities for the DUNE experiment, are also being submitted to other streams. △ Less

Submitted 29 March, 2025; originally announced March 2025.

Comments: Submitted to the 2026 Update of the European Strategy of Particle Physics

Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning

Authors: DUNE Collaboration, A. Abed Abud, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, F. Akbar, F. Alemanno, N. S. Alex, K. Allison, M. Alrashed, A. Alton, R. Alvarez, T. Alves, A. Aman, H. Amar, P. Amedo, J. Anderson, C. Andreopoulos , et al. (1313 additional authors not shown)

Abstract: The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolu… ▽ More The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours. △ Less

Submitted 26 June, 2025; v1 submitted 10 February, 2025; originally announced February 2025.

Comments: 32 pages, 18 figures

Report number: FERMILAB-PUB-25-0037-LBNF

Design and Implementation of the Cosmic Ray Tagger System for the ICARUS detector at FNAL

Authors: A. Aduszkiewicz, L. Bagby, B. Behera, P. Bernardini, S. Bertolucci, M. Betancourt, H. Budd, T. Boone, A. Campos, D. Casazza, V. Cicero, D. Cherdack, T. E. Coan, L. Degli Esposti, D. Di Ferdinando, L. Di Noto, C. Guandalini, M. Guerzoni, A. Heggestuen, C. Hilgenberg, R. Howell, M. Iliescu, G. Ingratta, T. Kim, U. Kose , et al. (28 additional authors not shown)

Abstract: The ICARUS-T600 Liquid Argon Time Projection Chamber is operating at Fermilab at shallow depth and thus exposed to a high flux of cosmic rays that can fake neutrino interactions. A cosmic ray tagging (CRT) system ($\sim$1100 m$^2$), surrounding the cryostat with two layers of fiber embedded plastic scintillators, was developed to mitigate the cosmic ray induced background. Using nanosecond-level t… ▽ More The ICARUS-T600 Liquid Argon Time Projection Chamber is operating at Fermilab at shallow depth and thus exposed to a high flux of cosmic rays that can fake neutrino interactions. A cosmic ray tagging (CRT) system ($\sim$1100 m$^2$), surrounding the cryostat with two layers of fiber embedded plastic scintillators, was developed to mitigate the cosmic ray induced background. Using nanosecond-level timing information, the CRT can distinguish incoming cosmic rays from outgoing particles from neutrino interactions in the TPC. In this paper an overview of the CRT system, its installation and commissioning at Fermilab, and its performance are discussed. △ Less

Submitted 6 January, 2025; originally announced January 2025.

The effect of a biosphere on the habitable timespan of stagnant-lid planets and implications for the atmospheric spectrum

Authors: Dennis Höning, Ludmila Carone, Philipp Baumeister, Kathy L. Chubb, John Lee Grenfell, Kaustubh Hakim, Nicolas Iro, Benjamin Taysum, Nicola Tosi

Abstract: Temperature-dependent biological productivity controls silicate weathering and thereby extends the potential habitable timespan of Earth. Models and theoretical considerations indicate that the runaway greenhouse on Earth-like exoplanets is generally accompanied by a dramatic increase in atmospheric H$_2$O and CO$_2$, which might be observed with the upcoming generation of space telescopes. If an… ▽ More Temperature-dependent biological productivity controls silicate weathering and thereby extends the potential habitable timespan of Earth. Models and theoretical considerations indicate that the runaway greenhouse on Earth-like exoplanets is generally accompanied by a dramatic increase in atmospheric H$_2$O and CO$_2$, which might be observed with the upcoming generation of space telescopes. If an active biosphere extends the habitable timespan of exoplanets similarly to Earth, observing the atmospheric spectra of exoplanets near the inner edge of the habitable zone could then give insights into whether the planet is inhabited. Here, we explore this idea for Earth-like stagnant-lid planets. We find that while for a reduced mantle, a surface biosphere extends the habitable timespan of the planet by about 1 Gyr, for more oxidising conditions, the biologically enhanced rate of weathering becomes increasingly compensated for by an increased supply rate of CO$_2$ to the atmosphere. Observationally, the resulting difference in atmospheric CO$_2$ near the inner edge of the habitable zone is clearly distinguishable between biotic planets with active weathering and abiotic planets that have experienced a runaway greenhouse. For an efficient hydrological cycle, the increased bioproductivity also leads to a CH$_4$ biosignature observable with JWST. As the planet becomes uninhabitable, the H$_2$O infrared absorption bands dominate, but the 4.3-micron CO$_2$ band remains a clear window into the CO$_2$ abundances. In summary, while the effect of life on the carbonate-silicate cycle leaves a record in the atmospheric spectrum of Earth-like stagnant-lid planets, future work is needed especially to determine the tectonic state and composition of exoplanets and to push forward the development of the next generation of space telescopes. △ Less

Submitted 16 December, 2024; originally announced December 2024.

H$_{2}$-H$_{2}$O demixing in Uranus and Neptune: Adiabatic structure models

Authors: Marina Cano Amoros, Nadine Nettelmann, Nicola Tosi, Philipp Baumeister, Heike Rauer

Abstract: Demixing properties of planetary major constituents influence the interior structure and evolution of planets. Comparing experimental and computational data on the miscibility of hydrogen and water to adiabatic profiles suggests phase separation between these components occurs in the ice giants Uranus and Neptune. We aim to predict the atmospheric water abundance and transition pressure between th… ▽ More Demixing properties of planetary major constituents influence the interior structure and evolution of planets. Comparing experimental and computational data on the miscibility of hydrogen and water to adiabatic profiles suggests phase separation between these components occurs in the ice giants Uranus and Neptune. We aim to predict the atmospheric water abundance and transition pressure between the water-poor outer envelope and the water-rich deep interior in Uranus and Neptune. We construct seven H2-H2O phase diagrams from the available experimental and computational data. We compute interior adiabatic structure models and compare these to the phase diagrams to infer whether demixing is occurring. We obtain a strong water depletion in the top layer due to rain-out of water and find upper limits on the atmospheric water mass fraction Z_atm of 0.21 for Uranus and 0.16 for Neptune. The transition from the water-poor to the water-rich layer is sharp and occurs at pressures P_Z between 4 and 11 GPa. Using these constraints on Z_atm and P_Z, we find that the observed gravitational harmonics J2 and J4 can be reproduced if P_Z > 10 GPa in Uranus and > 5 GPa in Neptune, and if the deep interior has a high primordial water mass fraction of 0.8, unless rocks are also present. The agreement with J4 is improved if rocks are confined deeper than P_Z, for instance below a rock cloud level at 2000 K (20-30 GPa). These findings confirm classical few-layer models and suggest that a layered structure may result from a combination of primordial mass accretion and subsequent phase separation. Reduced observational uncertainty in J4 and its dynamic contribution, atmospheric water abundance measurements from an Orbiter with a Probe mission to Uranus (UOP) or Neptune, and better understanding of the mixing behaviour of constituents are needed to constrain the interiors of ice giants. △ Less

Submitted 28 October, 2024; originally announced October 2024.

Comments: Accepted for publication in Astronomy and Astrophysics

Journal ref: A&A 692, A152 (2024)

The track-length extension fitting algorithm for energy measurement of interacting particles in liquid argon TPCs and its performance with ProtoDUNE-SP data

Authors: DUNE Collaboration, A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, F. Akbar, N. S. Alex, K. Allison, S. Alonso Monsalve, M. Alrashed, A. Alton, R. Alvarez, T. Alves, H. Amar, P. Amedo, J. Anderson, C. Andreopoulos , et al. (1348 additional authors not shown)

Abstract: This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy los… ▽ More This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions. △ Less

Submitted 26 December, 2024; v1 submitted 26 September, 2024; originally announced September 2024.

Report number: FERMILAB-PUB-24-0561-LBNF-PPD, CERN-EP-2024-256

A Comprehensive Evaluation of Deep Learning Object Detection Models on Heterogeneous Edge Devices

Authors: Daghash K. Alqahtani, Muhammad Aamir Cheema, Maria A. Rodriguez, Adel N. Toosi

Abstract: Modern applications such as autonomous vehicles, intelligent surveillance, and smart city systems increasingly require object detection on resource-constrained edge devices. Yet, there is still limited understanding of how different object detection models behave across heterogeneous edge devices and under varying scene complexity. In this paper, we benchmark YOLOv8 (Nano, Small, Medium), Efficien… ▽ More Modern applications such as autonomous vehicles, intelligent surveillance, and smart city systems increasingly require object detection on resource-constrained edge devices. Yet, there is still limited understanding of how different object detection models behave across heterogeneous edge devices and under varying scene complexity. In this paper, we benchmark YOLOv8 (Nano, Small, Medium), EfficientDet Lite (Lite0, Lite1, Lite2), and SSD (SSD MobileNet V1, SSDLite MobileDet) on Raspberry Pi 3, 4, 5 with/without Coral TPU accelerators, Raspberry Pi 5 with AI HAT+, Jetson Nano, and Jetson Orin Nano. We evaluate energy consumption, inference time, and accuracy, and further examine how accuracy changes with the number of objects in the input image. The results reveal clear trade-offs among accuracy, latency, and energy efficiency across model-device combinations. SSD MobileNet V1 achieves the lowest latency and energy consumption but the lowest accuracy, whereas YOLOv8 Medium achieves the highest accuracy at higher computational cost. TPU-based Raspberry Pi devices improve the efficiency of SSD and EfficientDet Lite while reducing YOLOv8 accuracy. Orin Nano offers the most favorable overall balance across most model families. The object-count-based analysis further shows that models achieve more similar accuracy on simpler images, while the accuracy gap widens as scene complexity increases. △ Less

Submitted 14 July, 2026; v1 submitted 25 September, 2024; originally announced September 2024.

Accelerating the discovery of steady-states of planetary interior dynamics with machine learning

Authors: Siddhant Agarwal, Nicola Tosi, Christian Hüttig, David S. Greenberg, Ali Can Bekar

Abstract: Simulating mantle convection often requires reaching a computationally expensive steady-state, crucial for deriving scaling laws for thermal and dynamical flow properties and benchmarking numerical solutions. The strong temperature dependence of the rheology of mantle rocks causes viscosity variations of several orders of magnitude, leading to a slow-evolving stagnant lid where heat conduction dom… ▽ More Simulating mantle convection often requires reaching a computationally expensive steady-state, crucial for deriving scaling laws for thermal and dynamical flow properties and benchmarking numerical solutions. The strong temperature dependence of the rheology of mantle rocks causes viscosity variations of several orders of magnitude, leading to a slow-evolving stagnant lid where heat conduction dominates, overlying a rapidly-evolving and strongly convecting region. Time-stepping methods, while effective for fluids with constant viscosity, are hindered by the Courant criterion, which restricts the time step based on the system's maximum velocity and grid size. Consequently, achieving steady-state requires a large number of time steps due to the disparate time scales governing the stagnant and convecting regions. We present a concept for accelerating mantle convection simulations using machine learning. We generate a dataset of 128 two-dimensional simulations with mixed basal and internal heating, and pressure- and temperature-dependent viscosity. We train a feedforward neural network on 97 simulations to predict steady-state temperature profiles. These can then be used to initialize numerical time stepping methods for different simulation parameters. Compared to typical initializations, the number of time steps required to reach steady-state is reduced by a median factor of 3.75. The benefit of this method lies in requiring very few simulations to train on, providing a solution with no prediction error as we initialize a numerical method, and posing minimal computational overhead at inference time. We demonstrate the effectiveness of our approach and discuss the potential implications for accelerated simulations for advancing mantle convection research. △ Less

Submitted 30 August, 2024; originally announced August 2024.

DUNE Phase II: Scientific Opportunities, Detector Concepts, Technological Solutions

Authors: DUNE Collaboration, A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, F. Akbar, K. Allison, S. Alonso Monsalve, M. Alrashed, A. Alton, R. Alvarez, T. Alves, H. Amar, P. Amedo, J. Anderson, C. Andreopoulos, M. Andreotti , et al. (1347 additional authors not shown)

Abstract: The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I… ▽ More The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos. △ Less

Submitted 22 August, 2024; originally announced August 2024.

Report number: FERMILAB-TM-2833-LBNF

Geodynamics of super-Earth GJ 486b

Authors: Tobias G. Meier, Dan J. Bower, Tim Lichtenberg, Mark Hammond, Paul J. Tackley, Raymond T. Pierrehumbert, José A. Caballero, Shang-Min Tsai, Megan Weiner Mansfield, Nicola Tosi, Philipp Baumeister

Abstract: Many super-Earths are on very short orbits around their host star and, therefore, more likely to be tidally locked. Because this locking can lead to a strong contrast between the dayside and nightside surface temperatures, these super-Earths could exhibit mantle convection patterns and tectonics that could differ significantly from those observed in the present-day solar system. The presence of an… ▽ More Many super-Earths are on very short orbits around their host star and, therefore, more likely to be tidally locked. Because this locking can lead to a strong contrast between the dayside and nightside surface temperatures, these super-Earths could exhibit mantle convection patterns and tectonics that could differ significantly from those observed in the present-day solar system. The presence of an atmosphere, however, would allow transport of heat from the dayside towards the nightside and thereby reduce the surface temperature contrast between the two hemispheres. On rocky planets, atmospheric and geodynamic regimes are closely linked, which directly connects the question of atmospheric thickness to the potential interior dynamics of the planet. Here, we study the interior dynamics of super-Earth GJ 486b ($R=1.34$ $R_{\oplus}$, $M=3.0$ $M_{\oplus}$, T$_\mathrm{eq}\approx700$ K), which is one of the most suitable M-dwarf super-Earth candidates for retaining an atmosphere produced by degassing from the mantle and magma ocean. We investigate how the geodynamic regime of GJ 486b is influenced by different surface temperature contrasts by varying possible atmospheric circulation regimes. We also investigate how the strength of the lithosphere affects the convection pattern. We find that hemispheric tectonics, the surface expression of degree-1 convection with downwellings forming on one hemisphere and upwelling material rising on the opposite hemisphere, is a consequence of the strong lithosphere rather than surface temperature contrast. Anchored hemispheric tectonics, where downwellings und upwellings have a preferred (day/night) hemisphere, is favoured for strong temperature contrasts between the dayside and nightside and higher surface temperatures. △ Less

Submitted 14 January, 2025; v1 submitted 20 August, 2024; originally announced August 2024.

Comments: 36 pages, 13 figures, updated with version accepted for publication in JGR: Planets

Journal ref: JGR: Planets 2024, 129, e2024JE008491

First Measurement of the Total Inelastic Cross-Section of Positively-Charged Kaons on Argon at Energies Between 5.0 and 7.5 GeV

Authors: DUNE Collaboration, A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, F. Akbar, K. Allison, S. Alonso Monsalve, M. Alrashed, A. Alton, R. Alvarez, T. Alves, H. Amar, P. Amedo, J. Anderson, C. Andreopoulos, M. Andreotti , et al. (1341 additional authors not shown)

Abstract: ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each… ▽ More ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting. △ Less

Submitted 1 August, 2024; originally announced August 2024.

Report number: CERN-EP-2024-211, FERMILAB-PUB-24-0216-V

Journal ref: Phys. Rev. D 110, (2024) 092011

Supernova Pointing Capabilities of DUNE

Authors: DUNE Collaboration, A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, M. Adamowski, D. Adams, M. Adinolfi, C. Adriano, A. Aduszkiewicz, J. Aguilar, B. Aimard, F. Akbar, K. Allison, S. Alonso Monsalve, M. Alrashed, A. Alton, R. Alvarez, T. Alves, H. Amar, P. Amedo, J. Anderson, D. A. Andrade , et al. (1340 additional authors not shown)

Abstract: The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electr… ▽ More The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on 40Ar and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called ``brems flipping'', as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE's burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage. △ Less

Submitted 24 December, 2025; v1 submitted 14 July, 2024; originally announced July 2024.

Comments: 26 pages, 17 figures

Report number: FERMILAB-PUB-24-0319-LBNF

Journal ref: Phys. Rev. D 111, 092006 (Published 12 May, 2025)