Showing 1–50 of 185 results for author: Pal, M

Dilation and Functional Models for Pure $\mathbfΘ_n$-Contractions and the von Neumann Inequality on Distinguished Varieties in $\mathbfΘ_n$

Authors: Aparna Gupta, Shubhankar Mandal Avijit Pal, Bhaskar Paul

Abstract: In this paper, we introduce the notion of a distinguished variety in the domain $\mathbfΘ_n$. One of the main results of the paper is a determinantal representation for every distinguished variety in $\mathbfΘ_n$. We also show that the closure of every distinguished variety is polynomially convex. Furthermore, we obtain a dilation and a functional model for a class of pure $\mathbfΘ_n$-contraction… ▽ More In this paper, we introduce the notion of a distinguished variety in the domain $\mathbfΘ_n$. One of the main results of the paper is a determinantal representation for every distinguished variety in $\mathbfΘ_n$. We also show that the closure of every distinguished variety is polynomially convex. Furthermore, we obtain a dilation and a functional model for a class of pure $\mathbfΘ_n$-contractions. Finally, we show that for a $\mathbfΘ_n$-contraction $\mathbf{T}=(T_1,\dots,T_n)$ such that $T_n^*$ is a pure contraction, there exists an algebraic variety in $\mathbfΘ_n$ for which the von Neumann inequality holds on the intersection of the closure of the variety with the distinguished boundary of $\mathbfΘ_n$. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Comments: This is initial version. Final version will submit soon

MSC Class: 14H50; 14M10; 47A20; 47A25; 32A60; 32M15

Graphic Matroid Secretary without the Graph

Authors: Paul Dütting, Renato Paes Leme, Martin Pál, Neel Patel

Abstract: The matroid secretary problem (MSP) is one of the cleanest, and most captivating open problems in online algorithms. The famous MSP conjecture stipulates that there exists a constant-competitive algorithm, yet to date the best known algorithms are $O(\log \log (\text{rank}))$ competitive. It is widely believed that all information that an algorithm for the MSP should use is information that is ava… ▽ More The matroid secretary problem (MSP) is one of the cleanest, and most captivating open problems in online algorithms. The famous MSP conjecture stipulates that there exists a constant-competitive algorithm, yet to date the best known algorithms are $O(\log \log (\text{rank}))$ competitive. It is widely believed that all information that an algorithm for the MSP should use is information that is available through an independence oracle on the already arrived elements. Despite this, there are natural classes of matroids where a constant-competitive algorithm is known if we are given additional upfront information about the matroid; while no such algorithm is known if all the algorithm can use is an independence oracle on the arrived elements. In this work, we tackle the perhaps most appealing such class of matroids, graphic matroids. We develop an algorithm for the MSP that has access to the independence oracle only. Our algorithm runs in polynomial time, and if the underlying matroid is graphic, it produces an independent set whose weight is at least $1/36$ of the maximum-weight independent set. Ours is the first constant-competitive algorithm for MSP on unknown graphic matroids. △ Less

Submitted 11 August, 2026; originally announced August 2026.

A proof of a conjecture on permutation polynomials

Authors: Krishna Mallick, Mohit Pal

Abstract: In this paper, we use finite fields and linear algebra methods to resolve a conjecture by T. Zhang, L. Zheng, H. Wang, J. Peng and Y. Li (Finite Fields Appl. 110 (2026) 102743) concerning permutation pentanomials. In this paper, we use finite fields and linear algebra methods to resolve a conjecture by T. Zhang, L. Zheng, H. Wang, J. Peng and Y. Li (Finite Fields Appl. 110 (2026) 102743) concerning permutation pentanomials. △ Less

Submitted 10 August, 2026; originally announced August 2026.

MSC Class: 12E20; 11T06

The emergence of X-ray emission lines during relativistic radio-jet formation in the changing-look active galactic nucleus 1ES 1927+654

Authors: Dev R. Sadaula, Sibasish Laha, Eileen T. Meyer, Onic I. Shuvo, Main Pal, Ritesh Ghosh, Matteo Guainazzi, Fabio Pacucci, Stefano Bianchi, Luigi Gallo, Rostom Mbarek, Amelia M. Hankla, Fabio La Franca, Tahir Yaqoob, Megan Masterson, Erin Kara, Missagh Mehdipour, Claudio Ricci, Javier A Garcia, Timothy R. Kallman, Ralf Ballhausen, Mitchell C. Begelman, Alexander Philippov, Suvendu Rakshit, Francesca Panessa , et al. (5 additional authors not shown)

Abstract: We present results from a comprehensive multi-wavelength monitoring campaign of the changing-look active galactic nucleus 1ES 1927+654 during the onset and evolution of a relativistic radio jet $\sim$(May 2022 - August 2025), using observations from XMM-Newton, Swift, TNG, ZTF, VLA, and VLBA. The soft X-ray emission lines at $\sim 0.56$ keV and $\sim 1$ keV have appeared with variable strength and… ▽ More We present results from a comprehensive multi-wavelength monitoring campaign of the changing-look active galactic nucleus 1ES 1927+654 during the onset and evolution of a relativistic radio jet $\sim$(May 2022 - August 2025), using observations from XMM-Newton, Swift, TNG, ZTF, VLA, and VLBA. The soft X-ray emission lines at $\sim 0.56$ keV and $\sim 1$ keV have appeared with variable strength and width during the formation of the nascent jet. We note that the $\sim 1$ keV feature has been persisting since the post-2017 flare phase. We also report the detection of a broad ($\sim 800$ eV) FeK emission feature at $(6-7)$ keV in the $\sim 70$ ks stacked EPIC-pn spectra, marking the first such detection, which historically was lacking in this source. The joint spectral fitting of XMM-Newton EPIC-pn and RGS data reveals the presence of ionized absorbers in 2022 ($\log{ξ\mathrm{/erg\ cm\ s^{-1}}}\sim 1.5\pm 0.3$, $\mathrm{N_H} \sim 2.5\pm 0.9\times 10^{20} \mathrm{cm^{-2}}$), but weaker than that detected during the high accretion state in 2018 (Eddington ratio, $λ_{\rm Edd}>1$). The absorption features further weakened in 2023-2025 and were marginally detectable ($\mathrm{N_H} \le 10^{20}\mathrm{cm^{-2}}$). The entire scenario is suggestive of a real-time transition of the accretion flow (from $λ_{\rm Edd}>1$ to $λ_{\rm Edd}\sim 0.3$) during which the winds become weaker and the jet starts to form and evolve. Furthermore, both the soft X-ray $(0.3-2)$ keV and 5 GHz radio fluxes, which increased by factors of $\sim 10$ and $\sim 60$, respectively, since 2022, have recently plateaued at elevated levels, indicating a stabilized accretion disk, corona, and jet configuration. △ Less

Submitted 14 August, 2026; v1 submitted 6 July, 2026; originally announced July 2026.

FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy

Authors: Debashis De, Mahua Nandy Pal, Dipankar Hazra

Abstract: Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to blindness of people. Detecting DR at the earliest stage is essential to prevent irreversible eye damage. Microaneurysm dots are the first signs of DR. As the dots are tiny and of low contrast, detecting mild DR is a very challenging task. Federated learning (FL) preserves data privacy, which is a major concern for medi… ▽ More Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to blindness of people. Detecting DR at the earliest stage is essential to prevent irreversible eye damage. Microaneurysm dots are the first signs of DR. As the dots are tiny and of low contrast, detecting mild DR is a very challenging task. Federated learning (FL) preserves data privacy, which is a major concern for medical image processing. FL is a collaborative learning method, which shares only the model parameters with a server, without sharing the patient data to a central server. Inspired by classical FL, we propose a federated learning-based quantum neural network (federated QNN) for this task. We implemented the models with limited samples and few learnable parameters from the E-ophtha and Retina MNIST datasets. The crossevaluation efficiency of the proposed federated quantum neural network system for privacy-preserving early detection of diabetic retinopathy (FQPDR) in Kaggle dataset images indicates the robustness of the light weight learning models. FQPDR performances are inspiring while considering existing non-FL and FL methods. △ Less

Submitted 8 May, 2026; originally announced May 2026.

Journal ref: Evolutionary Intelligence (17) 4047 4068 (2024)

Unsharp Measurement with Adaptive Gaussian POVMs for Quantum-Inspired Image Processing

Authors: Debashis Saikia, Bikash K. Behera, Mayukha Pal, Prasanta K. Panigrahi

Abstract: We propose a data-adaptive probabilistic intensity remapping framework for structure-preserving transformation of grayscale images. The suggested method formulates intensity transformation as a continuous, data-driven remapping process, in contrast to traditional histogram-based techniques that rely on hard thresholding and generate piecewise-constant mappings. The image statistics yield represent… ▽ More We propose a data-adaptive probabilistic intensity remapping framework for structure-preserving transformation of grayscale images. The suggested method formulates intensity transformation as a continuous, data-driven remapping process, in contrast to traditional histogram-based techniques that rely on hard thresholding and generate piecewise-constant mappings. The image statistics yield representative intensity values, and Gaussian-based weighting methods probabilistically allocate each pixel to several components. Smooth transitions while preserving structural features are achieved by computing the output intensity as an expectation over these components. A smooth transition from soft probabilistic remapping to hard assignment is made possible by the introduction of a nonlinear sharpening parameter $γ$ to regulate the degree of localization. This offers clear control over the trade-off between intensity discrimination and smoothing. Furthermore, the resolution of the remapping function is determined by the number of components $k$. When compared to thresholding-based methods, experimental results on standard benchmark images show that the suggested method achieves better structural fidelity and controlled information reduction as measured by PSNR, SSIM, and entropy. Overall, by allowing continuous, probabilistic intensity modifications, the framework provides a robust and efficient substitute for discrete thresholding. △ Less

Submitted 23 April, 2026; v1 submitted 6 April, 2026; originally announced April 2026.

Compact system development of efficient quantum-entangled photon sources towards deployable and industrial devices

Authors: Yared G. Zena, Moritz Langer, Ahmad Rahimi, Abhishikth Dhurjati, Pavel Ruchka, Sara Jakovljevic, Mandira Pal, Frank H. P. Fitzek, Harald Giessen, Juergen Czarske, Riccardo Bassoli, Caspar Hopfmann

Abstract: Entangled photon pair sources are a key enabling technology for quantum communication and networking, yet their deployment beyond laboratory environments is hindered by system-level complexity, limited operational stability, and insufficient industry compatibility. Here, we demonstrate a rack-based, mobile quantum light source architecture based on a semiconductor quantum dot emitter that directly… ▽ More Entangled photon pair sources are a key enabling technology for quantum communication and networking, yet their deployment beyond laboratory environments is hindered by system-level complexity, limited operational stability, and insufficient industry compatibility. Here, we demonstrate a rack-based, mobile quantum light source architecture based on a semiconductor quantum dot emitter that directly addresses these challenges through modular system integration and automated operation. The source generates polarization-entangled photon pairs with an entanglement negativity 2n of up to $0.98(1)$, confirming near-maximal entanglement quality. In continuous, hands-off operation over a six-hour time window, the system achieves an average single-photon emission rate of $697(8)$ kHz and a maximum rate of $740(7)$ kHz, while maintaining 2n-value of more than $95$ $\%$. These results are enabled by the integration of optical excitation, collection, cryogenic operation, and control electronics within a standardized rack footprint, together with automated monitoring. By demonstrating simultaneously high entanglement quality, sustained brightness, and long-term operational stability in an industry-aligned system architecture, this work advances semiconductor quantum dot sources toward deployable entangled photon sources for applied quantum photonics. △ Less

Submitted 2 April, 2026; originally announced April 2026.

Comments: 22 Pages, 10 figures

Inclusive jet cross section in $pp$ collisions at $\sqrt{s} = 200$ and $510$ GeV

Authors: STAR Collaboration, B. E. Aboona, J. Adam, L. Adamczyk, I. Aggarwal, M. M. Aggarwal, Z. Ahammed, A. K. Alshammri, E. C. Aschenauer, S. Aslam, J. Atchison, V. Bairathi, X. Bao, P. Barik, K. Barish, S. Behera, R. Bellwied, P. Bhagat, A. Bhasin, S. Bhatta, S. R. Bhosale, J. Bielcik, J. Bielcikova, J. D. Brandenburg, C. Broodo , et al. (379 additional authors not shown)

Abstract: Jets are collimated clusters of particles formed by the hadronization of partons following a hard interaction. In proton-proton ($pp$) collisions at the Relativistic Heavy Ion Collider (RHIC), jet production is dominated by $gg$ and $qg$ partonic processes, allowing us to directly probe the gluon parton distribution function (PDF) in the proton in a way complementary to deep inelastic scattering.… ▽ More Jets are collimated clusters of particles formed by the hadronization of partons following a hard interaction. In proton-proton ($pp$) collisions at the Relativistic Heavy Ion Collider (RHIC), jet production is dominated by $gg$ and $qg$ partonic processes, allowing us to directly probe the gluon parton distribution function (PDF) in the proton in a way complementary to deep inelastic scattering. In this paper, we report the double-differential inclusive-jet cross sections as a function of jet transverse momentum, $p_{\rm T}$, and pseudorapidity, $η$, at center-of-mass energies $\sqrt{s} = 200$ and $510$~GeV, from $pp$ collisions studied with the STAR detector. The jet $p_{\rm T}$ is corrected for underlying event contributions by applying an off-axis cone method. At mid-pseudorapidity, $|η| < 0.9$, the kinematic coverage of our data extends to $0.07 < x_{\rm T} \text{ (}= 2p_{\rm T}{} / \sqrt{s} \text{)} < 0.5$ and $0.03 < x_{\rm T} < 0.31$ at $\sqrt{s} = 200$~and 510 GeV, respectively, where the gluon PDF is poorly constrained by the TeV-scale $pp$~($p\bar{p}$) colliders. The inclusive jet cross sections are compared to the next-to-next-to-leading order perturbative quantum chromodynamics calculations using several recent PDF sets as inputs. These results will further constrain the gluon PDF, help tune Monte Carlo generators, and provide critical reference data needed to study the quark-gluon plasma. △ Less

Submitted 24 June, 2026; v1 submitted 30 March, 2026; originally announced March 2026.

FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System

Authors: Kriti Thakur, Alivelu Manga Parimi, Mayukha Pal

Abstract: Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energy resources (DERs), which inject greater variability and complexity into grid operations. In this study, FaultXformer is proposed, a Transformer encoder-based architecture developed for automatic fault analysis using real-time current data obtained… ▽ More Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energy resources (DERs), which inject greater variability and complexity into grid operations. In this study, FaultXformer is proposed, a Transformer encoder-based architecture developed for automatic fault analysis using real-time current data obtained from phasor measurement unit (PMU). The approach utilizes time-series current data to initially extract rich temporal information in stage 1, which is crucial for identifying the fault type and precisely determining its location across multiple nodes. In Stage 2, these extracted features are processed to differentiate among distinct fault types and identify the respective fault location within the distribution system. Thus, this dual-stage transformer encoder pipeline enables high-fidelity representation learning, considerably boosting the performance of the work. The model was validated on a dataset generated from the IEEE 13-node test feeder, simulated with 20 separate fault locations and several DER integration scenarios, utilizing current measurements from four strategically located PMUs. To demonstrate robust performance evaluation, stratified 10-fold cross-validation is performed. FaultXformer achieved average accuracies of 98.76% in fault type classification and 98.92% in fault location identification across cross-validation, consistently surpassing conventional deep learning baselines convolutional neural network (CNN), recurrent neural network (RNN). long short-term memory (LSTM) by 1.70%, 34.95%, and 2.04% in classification accuracy and by 10.82%, 40.89%, and 6.27% in location accuracy, respectively. These results demonstrate the efficacy of the proposed model with significant DER penetration. △ Less

Submitted 27 February, 2026; originally announced February 2026.

Data-Driven Linearization based Arc Fault Prediction in Medium Voltage Electrical Distribution System

Authors: Mihir Sinha, Kriti Thakur, Prasanta K. Panigrahi, Alivelu Manga Parimi, Mayukha Pal

Abstract: High-impedance arc faults (HIAFs) in medium-voltage electrical distribution systems are difficult to detect due to their low fault current levels and nonlinear transient behavior. Traditional detection algorithms generally struggle with predictions under dynamic waveform scenarios. This research provides our approach of using a unique data-driven linearization (DDL) framework for early prediction… ▽ More High-impedance arc faults (HIAFs) in medium-voltage electrical distribution systems are difficult to detect due to their low fault current levels and nonlinear transient behavior. Traditional detection algorithms generally struggle with predictions under dynamic waveform scenarios. This research provides our approach of using a unique data-driven linearization (DDL) framework for early prediction of HIAFs, giving both interpretability and scalability. The proposed method translates nonlinear current waveforms into a linearized space using coordinate embeddings and polynomial transformation, enabling precise modelling of fault precursors.The total duration of the test waveform is 0.5 seconds, within which the arc fault occurs between 0.2 seconds to 0.3 seconds. Our proposed approach using DDL, trained solely on the pre-fault healthy region (0.10 seconds to 0.18 seconds) effectively captures certain invisible fault precursors, to accurately predict the onset of fault at 0.189 seconds, which is approximately 0.011 seconds (i.e., 11 milliseconds) earlier than the actual fault occurrence. In particular, the framework predicts the start of arc faults at 0.189 seconds, significantly earlier of the actual fault incidence at 0.200 seconds, demonstrating substantial early warning capability. Performance evaluation comprises eigenvalue analysis, prediction error measures, error growth rate and waveform regeneration fidelity. Such early prediction proves that the model is capable of correctly foreseeing faults which is especially helpful in preventing real-world faults and accidents. It confirms that our proposed approach reliably predicts arc faults in medium-voltage power distribution systems △ Less

Submitted 27 February, 2026; originally announced February 2026.

Fault Detection in Electrical Distribution System using Autoencoders

Authors: Sidharthenee Nayak, Victor Sam Moses Babu, Chandrashekhar Narayan Bhende, Pratyush Chakraborty, Mayukha Pal

Abstract: In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their practical application remains highly challenging. Given the probabilistic nature of fault occurrences and… ▽ More In recent times, there has been considerable interest in fault detection within electrical power systems, garnering attention from both academic researchers and industry professionals. Despite the development of numerous fault detection methods and their adaptations over the past decade, their practical application remains highly challenging. Given the probabilistic nature of fault occurrences and parameters, certain decision-making tasks could be approached from a probabilistic standpoint. Protective systems are tasked with the detection, classification, and localization of faulty voltage and current line magnitudes, culminating in the activation of circuit breakers to isolate the faulty line. An essential aspect of designing effective fault detection systems lies in obtaining reliable data for training and testing, which is often scarce. Leveraging deep learning techniques, particularly the powerful capabilities of pattern classifiers in learning, generalizing, and parallel processing, offers promising avenues for intelligent fault detection. To address this, our paper proposes an anomaly-based approach for fault detection in electrical power systems, employing deep autoencoders. Additionally, we utilize Convolutional Autoencoders (CAE) for dimensionality reduction, which, due to its fewer parameters, requires less training time compared to conventional autoencoders. The proposed method demonstrates superior performance and accuracy compared to alternative detection approaches by achieving an accuracy of 97.62% and 99.92% on simulated and publicly available datasets. △ Less

Submitted 16 February, 2026; originally announced February 2026.

A Serendipitous NuSTAR Detection of a Giant Radio Source Harboring an Obscured Active Galactic Nucleus

Authors: Vaidehi S. Paliya, S. Marchesi, X. Zhao, D. J. Saikia, Moumita Pal, Somak Raychaudhury

Abstract: Giant radio sources (GRSs) harbor the Universe's largest structures generated by individual galaxies, with projected source sizes exceeding 700 kpc. These enigmatic objects have been mainly studied at radio frequencies, and their physical properties in the high-energy domain are poorly understood. Here we present the results of a multiwavelength study focused on NuSTAR J112829+5831.8 (J1128+5831),… ▽ More Giant radio sources (GRSs) harbor the Universe's largest structures generated by individual galaxies, with projected source sizes exceeding 700 kpc. These enigmatic objects have been mainly studied at radio frequencies, and their physical properties in the high-energy domain are poorly understood. Here we present the results of a multiwavelength study focused on NuSTAR J112829+5831.8 (J1128+5831), the only known GRS serendipitously detected with the Nuclear Spectroscopic Telescope Array. Being located in proximity to the famous interacting galaxy system, Arp 299, J1128+5831 has been serendipitously observed also by the Chandra X-ray Observatory, Hubble Space Telescope, and XMM-Newton satellites. From radio observations with the Low Frequency Array, the NRAO VLA Sky Survey and the Very Large Array Sky Survey, we have determined that J1128+5831 has an overall steep radio spectrum ($α=-0.86$; $F_ν\proptoν^α$) and a low core dominance ($C_{\rm D}=-2.4$, in log-scale), indicating the source to be viewed at large angles. From the X-ray spectral analysis, we found J1128+5831 to harbor an obscured active galactic nucleus (AGN) with neutral hydrogen column density exceeding $10^{23}$ cm$^{-2}$. Its optical spectrum, taken with the Dark Energy Spectroscopic Instrument, exhibits prominent narrow emission lines but lacks broad components, thus confirming J1128+5831 to be a Type 2 AGN powered by a radiatively efficient accreting system. Overall, the broadband properties of J1128+5831 are consistent with those observed for the general GRS population. △ Less

Submitted 2 February, 2026; originally announced February 2026.

Comments: ApJ, in press

Probing Late-Stage Hadronic Interactions at High Baryon Density via $K^{*0}$ Production in the RHIC Beam Energy Scan Program

Authors: STAR Collaboration, B. E. Aboona, J. Adam, G. Agakishiev, I. Aggarwal, M. M. Aggarwal, Z. Ahammed, A. Aitbayev, I. Alekseev, E. Alpatov, A. K. Alshammri, A. Aparin, S. Aslam, J. Atchison, G. S. Averichev, V. Bairathi, X. Bao, P. Barik, K. Barish, S. Behera, P. Bhagat, A. Bhasin, S. Bhatta, I. G. Bordyuzhin, J. D. Brandenburg , et al. (363 additional authors not shown)

Abstract: A precision measurement of the $K^{*0}$ meson yield is reported in Au+Au collisions at $\sqrt{s_{NN}} = 7.7,\; 11.5,\; 14.6,\; 19.6,$ and $27~\mathrm{GeV}$ using the high-statistics data sample collected by the STAR experiment during the Beam Energy Scan II (BES-II) program at RHIC. The transeverse momentum ($p_{T}$)-integrated yield ratios $(K^{*0} + \overline{K^{*0}})/(K^{+} + K^{-})$ in central… ▽ More A precision measurement of the $K^{*0}$ meson yield is reported in Au+Au collisions at $\sqrt{s_{NN}} = 7.7,\; 11.5,\; 14.6,\; 19.6,$ and $27~\mathrm{GeV}$ using the high-statistics data sample collected by the STAR experiment during the Beam Energy Scan II (BES-II) program at RHIC. The transeverse momentum ($p_{T}$)-integrated yield ratios $(K^{*0} + \overline{K^{*0}})/(K^{+} + K^{-})$ in central collisions show a suppression relative to peripheral collisions at the $(1.7\text{-}3.6)\,σ$ level, while a thermal model without final-stage rescattering overpredicts this ratio with a deviation of $(6.9\text{-}8.2)\,σ$. These results indicate a loss of the measured $K^{*0}$ signal in central collisions due to re-scattering of its hadronic decay products in the hadronic phase. The $p_{T}$-integrated yield of charged kaons exhibits an approximate scaling with charged-particle multiplicity, independent of collision energy and system size. A similar trend is observed for the short-lived $K^{*0}$ resonance, although significant deviations emerge at lower energies. At BES energies, the $K^{*0}/K$ ratio shows stronger suppression than at the highest RHIC and LHC energies within a given multiplicity bin, particularly in central and mid-central collisions. This behavior is consistent with changes in the effective hadronic interaction cross section and is supported by transport model calculations, which indicate dominant meson-baryon interactions at lower energies and meson-meson interactions at higher energies. △ Less

Submitted 28 April, 2026; v1 submitted 21 January, 2026; originally announced January 2026.

Comments: 17 pages, 6 figures, 2 tables

Journal ref: Phys. Lett. B 876 (2026) 140389

A Boolean Function-Theoretic Framework for Expressivity in GNNs with Applications to Fair Graph Mining

Authors: Manjish Pal

Abstract: We propose a novel expressivity framework for Graph Neural Networks (GNNs) grounded in Boolean function theory, enabling a fine-grained analysis of their ability to capture complex subpopulation structures. We introduce the notion of \textit{Subpopulation Boolean Isomorphism} (SBI) as an invariant that strictly subsumes existing expressivity measures such as Weisfeiler-Lehman (WL), biconnectivity-… ▽ More We propose a novel expressivity framework for Graph Neural Networks (GNNs) grounded in Boolean function theory, enabling a fine-grained analysis of their ability to capture complex subpopulation structures. We introduce the notion of \textit{Subpopulation Boolean Isomorphism} (SBI) as an invariant that strictly subsumes existing expressivity measures such as Weisfeiler-Lehman (WL), biconnectivity-based, and homomorphism-based frameworks. Our theoretical results identify Fourier degree, circuit class (AC$^0$, NC$^1$), and influence as key barriers to expressivity in fairness-aware GNNs. We design a circuit-traversal-based fairness algorithm capable of handling subpopulations defined by high-complexity Boolean functions, such as parity, which break existing baselines. Experiments on real-world graphs show that our method achieves low fairness gaps across intersectional groups where state-of-the-art methods fail, providing the first principled treatment of GNN expressivity tailored to fairness. △ Less

Submitted 19 January, 2026; originally announced January 2026.

SMART SLM: Structured Memory and Reasoning Transformer, A Small Language Model for Accurate Document Assistance

Authors: Divij Dudeja, Mayukha Pal

Abstract: The user of Engineering Manuals (EM) finds it difficult to read EM s because they are long, have a dense format which includes written documents, step by step procedures, and standard parameter lists for engineering equipment. Off the shelf transformers, especially compact ones, treat this material as a flat stream of tokens. This approach leads to confident but incorrect numeric answers and force… ▽ More The user of Engineering Manuals (EM) finds it difficult to read EM s because they are long, have a dense format which includes written documents, step by step procedures, and standard parameter lists for engineering equipment. Off the shelf transformers, especially compact ones, treat this material as a flat stream of tokens. This approach leads to confident but incorrect numeric answers and forces the models to memorize separate facts inefficiently. SMART (Structured Memory and Reasoning Transformer) offers a different and practical solution to the above problem. SMART structures its processing by using a hierarchical approach, and is based upon three main job categories (1) A syntax-aware Fact Extractor (Grammarian) Tree LSTM which extracts facts as subject relation object relations from EM sentences (2) A compact indexed memory MANN (Memory Augmented Neural Network) that indexes these Rational Subject Relation Objects as 384 dimensional vectors that are associated with the source of the information, and (3) A 6 layer Transformer that learns to fuse the previously retrieved facts into its generated response. The entire SMART model utilizes 45.51M parameters, which is 64% less than GPT-2 (124M) and 69% less than BERT (133M), and it achieves a 21.3% higher accuracy than GPT-2, indicating that SMART fits the data better with the least amount of processing requirements. SMART employs dual modes of inference an indexed fast path for known documents (sub-second answer times) and an indexed dynamic path assisted by RAGs for new uploads (FAISS Top 20 results with memory severed at 64 slots). In real world deployment, this framework leads to more well supported results with reduced hallucinations than comparable small transformer models. △ Less

Submitted 24 December, 2025; originally announced December 2025.

Identified charged hadron production in Au+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 54.4 GeV with the STAR detector

Authors: STAR Collaboration, B. E. Aboona, J. Adam, G. Agakishiev, I. Aggarwal, M. M. Aggarwal, Z. Ahammed, A. Aitbayev, I. Alekseev, E. Alpatov, A. K. Alshammri, A. Aparin, S. Aslam, J. Atchison, G. S. Averichev, V. Bairathi, X. Bao, P. Barik, K. Barish, S. Behera, P. Bhagat, A. Bhasin, S. Bhatta, I. G. Bordyuzhin, J. D. Brandenburg , et al. (363 additional authors not shown)

Abstract: We present results on the production of $π^{\pm}$, $K^{\pm}$, $p$, and $\bar{p}$ in Au+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 54.4~GeV using the STAR detector at RHIC, at midrapidity ($|y| <$ 0.1). Invariant yields of these particles as a function of transverse momentum are shown. We determine bulk properties such as integrated particle yields ($dN/dy$), mean transverse momentum (… ▽ More We present results on the production of $π^{\pm}$, $K^{\pm}$, $p$, and $\bar{p}$ in Au+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 54.4~GeV using the STAR detector at RHIC, at midrapidity ($|y| <$ 0.1). Invariant yields of these particles as a function of transverse momentum are shown. We determine bulk properties such as integrated particle yields ($dN/dy$), mean transverse momentum ($\langle p_{T} \rangle$), particle ratios, which provide insight into the particle production mechanisms. Additionally, the kinetic freezeout parameters ($T_\text{kin}$ and $\langle β_{T} \rangle$), which provide information about the dynamics of the system at the time of freezeout, are obtained. The Bjorken energy density ($ε_{\rm{BJ}}$), which gives an estimate of the energy density in the central rapidity region of the collision zone at the formation time $τ$, is calculated and presented as a function of multiplicity for various energies. The results are compared with those from the models such as A Multi-Phase Transport (AMPT) and Heavy Ion Jet INteraction Generator (HIJING) for further insights. △ Less

Submitted 21 May, 2026; v1 submitted 6 December, 2025; originally announced December 2025.

Comments: 14 pages, 16 figures

Journal ref: Physical Review C 113, 054907 (2026)

Study of Central Exclusive Production of $π^+π^-$, $K^+K^-$ and $p \bar{p}$ Pairs in Proton-Proton Collisions at $\sqrt{s} = 510$ GeV with the STAR Detector at RHIC

Authors: STAR Collaboration, B. E. Aboona, J. Adam, L. Adamczyk, I. Aggarwal, M. M. Aggarwal, Z. Ahammed, A. K. Alshammri, E. C. Aschenauer, S. Aslam, J. Atchison, V. Bairathi, X. Bao, P. Barik, K. Barish, S. Behera, R. Bellwied, P. Bhagat, A. Bhasin, S. Bhatta, S. R. Bhosale, J. Bielcik, J. Bielcikova, J. D. Brandenburg, C. Broodo , et al. (380 additional authors not shown)

Abstract: We report on the first measurement of the Central Exclusive Production process in proton-proton collisions: $pp \ \rightarrow \ p h^+ h^- p$ (where $h = π, K, p$) at the center-of-mass energy $\sqrt{s} = 510$ GeV with the STAR experiment at RHIC. At this energy, the process is dominated by a double Pomeron exchange mechanism. Hence, it provides a clean environment for investigating Pomeron interac… ▽ More We report on the first measurement of the Central Exclusive Production process in proton-proton collisions: $pp \ \rightarrow \ p h^+ h^- p$ (where $h = π, K, p$) at the center-of-mass energy $\sqrt{s} = 510$ GeV with the STAR experiment at RHIC. At this energy, the process is dominated by a double Pomeron exchange mechanism. Hence, it provides a clean environment for investigating Pomeron interactions by measuring fully reconstructed final states involving only two hadrons and two forward scattered protons. The oppositely charged hadron pairs are measured within the central detector of STAR. The forward scattered protons are measured in the Roman Pot system allowing the verification of the event's exclusivity. Differential fiducial cross sections within the STAR acceptance are presented as a function of the difference in the azimuthal angle between the outgoing protons. The invariant masses of the charged hadron pairs are measured up to approximately 3 GeV and the square of the four-momentum transfer ($t_1$ and $t_2$) of the two forward-scattered protons in the range $0.3 \text{ GeV}^2 < -t_1 , -t_2 < 1.6 \text{ GeV}^2$. The differential fiducial cross sections of the forward protons as a function of the $|t_1 + t_2|$ are also presented. All results for the $π^+π^-$ pair are presented in three mass ranges. A comparison with GRANIITTI Monte Carlo predictions are also presented, where the spectra include continuum and resonant contributions. The observed spectra are consistent with double Pomeron exchange, including resonances seen in previous studies. △ Less

Submitted 2 June, 2026; v1 submitted 31 October, 2025; originally announced October 2025.

Comments: Prepared for submission to JHEP

Measurement of transverse polarization of $Λ$ and $\barΛ$ hyperons inside jets in $pp$ collisions at $\sqrt{s}=200$ GeV

Authors: STAR Collaboration, B. E. Aboona, J. Adam, G. Agakishiev, I. Aggarwal, M. M. Aggarwal, Z. Ahammed, A. Aitbayev, I. Alekseev, E. Alpatov, A. K. Alshammri, A. Aparin, S. Aslam, J. Atchison, G. S. Averichev, V. Bairathi, X. Bao, P. Barik, K. Barish, S. Behera, P. Bhaga t, A. Bhasin, S. Bhatta, I. G. Bordyuzhin, J. D. Brandenburg , et al. (365 additional authors not shown)

Abstract: A surprisingly large transverse polarization of $Λ$ hyperons in unpolarized hadron-nucleon/nucleus collisions has been observed for 50 years, and the origin of this polarization remains an important open question. Recently, theoretical frameworks have advanced in describing this puzzle with the polarizing fragmentation function (PFF). We report the first measurement of $Λ$ and $\overlineΛ$ transve… ▽ More A surprisingly large transverse polarization of $Λ$ hyperons in unpolarized hadron-nucleon/nucleus collisions has been observed for 50 years, and the origin of this polarization remains an important open question. Recently, theoretical frameworks have advanced in describing this puzzle with the polarizing fragmentation function (PFF). We report the first measurement of $Λ$ and $\overlineΛ$ transverse polarization inside jets in unpolarized proton-proton collisions, which is directly attributed to the PFF. The polarization is measured as a function of the jet transverse momentum, the fraction of the jet momentum carried by $Λ$($\overlineΛ$) hyperons, and the transverse momentum of $Λ(\overlineΛ)$ hyperons relative to the jet axis. Covering a wide jet-energy range, these data provide the first constraints on the gluon PFF and allow tests of TMD evolution and its universality. △ Less

Submitted 13 May, 2026; v1 submitted 22 September, 2025; originally announced September 2025.

Journal ref: J. High Energ. Phys. 2026, 50 (2026)

Observation of charmonium sequential suppression in heavy-ion collisions at the Relativistic Heavy Ion Collider

Authors: STAR Collaboration, B. E. Aboona, J. Adam, L. Adamczyk, I. Aggarwal, M. M. Aggarwal, Z. Ahammed, A. K. Alshammri, E. C. Aschenauer, S. Aslam, J. Atchison, V. Bairathi, X. Bao, P. Barik, K. Barish, S. Behera, R. Bellwied, P. Bhagat, A. Bhasin, S. Bhatta, S. R. Bhosale, J. Bielcik, J. Bielcikova, J. D. Brandenburg, C. Broodo , et al. (372 additional authors not shown)

Abstract: We report measurements of charmonium sequential suppression in Ru+Ru and Zr+Zr collisions at $\sqrt{s_{\mathrm {NN}}}$ = 200 GeV with the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The inclusive yield ratio of $ψ$(2S) to J/$ψ$ as a function of transverse momentum is reported, along with the centrality dependence of the double ratio, defined as the $ψ$(2S) to J/$ψ$ ratio in heav… ▽ More We report measurements of charmonium sequential suppression in Ru+Ru and Zr+Zr collisions at $\sqrt{s_{\mathrm {NN}}}$ = 200 GeV with the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The inclusive yield ratio of $ψ$(2S) to J/$ψ$ as a function of transverse momentum is reported, along with the centrality dependence of the double ratio, defined as the $ψ$(2S) to J/$ψ$ ratio in heavy-ion collisions relative to that in $p$+$p$ collisions. In the 0-80% centrality class, the double ratio is found to be 0.41 $\pm$ 0.10 (stat) $\pm$ 0.03 (syst) $\pm$ 0.02 (ref), lower than unity with a significance of 5.6 standard deviations. This provides experimental evidence that $ψ$(2S) is significantly more suppressed than J/$ψ$ in heavy-ion collisions at RHIC. This sequential suppression pattern seems to increase from peripheral to central collisions, but with no significant dependence on the transverse momentum. △ Less

Submitted 9 March, 2026; v1 submitted 16 September, 2025; originally announced September 2025.

Journal ref: Phys. Rev. Lett. 136, 122302 (2026)

Game-Theoretic Resilience Framework for Cyber-Physical Microgrids using Multi-Agent Reinforcement Learning

Authors: S Krishna Niketh, Sagar Babu Mitikiri, V Vignesh, Vedantham Lakshmi Srinivas, Mayukha Pal

Abstract: The increasing reliance on cyber physical infrastructure in modern power systems has amplified the risk of targeted cyber attacks, necessitating robust and adaptive resilience strategies. This paper presents a mathematically rigorous game theoretic framework to evaluate and enhance microgrid resilience using a combination of quantitative resilience metrics Load Served Ratio LSR, Critical Load Resi… ▽ More The increasing reliance on cyber physical infrastructure in modern power systems has amplified the risk of targeted cyber attacks, necessitating robust and adaptive resilience strategies. This paper presents a mathematically rigorous game theoretic framework to evaluate and enhance microgrid resilience using a combination of quantitative resilience metrics Load Served Ratio LSR, Critical Load Resilience CLR, Topological Survivability Score TSS, and DER Resilience Score DRS. These are integrated into a unified payoff matrix using the Analytic Hierarchy Process AHP to assess attack defense interactions. The framework is formalized as a finite horizon Markov Decision Process MDP with formal convergence guarantees and computational complexity bounds. Three case studies are developed 1. static attacks analyzed via Nash equilibrium, 2. severe attacks incorporating high impact strategies, and 3. adaptive attacks using Stackelberg games, regret matching, softmax heuristics, and Multi Agent Q Learning. Rigorous theoretical analysis provides convergence proofs with explicit rates , PAC learning sample complexity bounds, and computational complexity analysis. The framework is tested on an enhanced IEEE 33bus distribution system with DERs and control switches, demonstrating the effectiveness of adaptive and strategic defenses in improving cyber physical resilience with statistically significant improvements of 18.7% 2.1% over static approaches. △ Less

Submitted 10 September, 2025; originally announced September 2025.

Handling imbalance and few-sample size in ML based Onion disease classification

Authors: Abhijeet Manoj Pal, Rajbabu Velmurugan

Abstract: Accurate classification of pests and diseases plays a vital role in precision agriculture, enabling efficient identification, targeted interventions, and preventing their further spread. However, current methods primarily focus on binary classification, which limits their practical applications, especially in scenarios where accurately identifying the specific type of disease or pest is essential.… ▽ More Accurate classification of pests and diseases plays a vital role in precision agriculture, enabling efficient identification, targeted interventions, and preventing their further spread. However, current methods primarily focus on binary classification, which limits their practical applications, especially in scenarios where accurately identifying the specific type of disease or pest is essential. We propose a robust deep learning based model for multi-class classification of onion crop diseases and pests. We enhance a pre-trained Convolutional Neural Network (CNN) model by integrating attention based modules and employing comprehensive data augmentation pipeline to mitigate class imbalance. We propose a model which gives 96.90% overall accuracy and 0.96 F1 score on real-world field image dataset. This model gives better results than other approaches using the same datasets. △ Less

Submitted 1 September, 2025; originally announced September 2025.

Comments: 6 pages, 8 figures

NIR/VIS dual-comb spectroscopy comparing high and low repetition rate regimes

Authors: Alexander Eber, Mithun Pal, Lukas Fürst, Emily Hruska, Christoph Gruber, Marcus Ossiander, Birgitta Bernhardt

Abstract: Dual-comb spectroscopy enables broadband analysis of key molecules with unparalleled frequency resolution and exceptional signal-to-noise ratios across various spectral regions. However, fully harnessing its potential for broadband spectroscopy with high sensitivity and spectral resolution depends critically on selecting the appropriate frequency combs with optimized (comb) parameters tailored to… ▽ More Dual-comb spectroscopy enables broadband analysis of key molecules with unparalleled frequency resolution and exceptional signal-to-noise ratios across various spectral regions. However, fully harnessing its potential for broadband spectroscopy with high sensitivity and spectral resolution depends critically on selecting the appropriate frequency combs with optimized (comb) parameters tailored to specific applications. This study compares dual-comb spectroscopy systems operating at 80 MHz and 1 GHz repetition rates, in the near infrared and visible spectral regions. The 80 MHz system provides high spectral resolution, ideal for resolving complex spectra, showcased with measurements of NH$_3$ vibrational bands and I$_2$ hyperfine transitions. Utilizing phase-locked feed-forward stabilization, the system delivers excellent signal-to-noise ratios but faces limitations in temporal resolution. The free-running 1 GHz system offers superior temporal resolution and compactness, making it suitable for real-time environmental monitoring in laboratory and field settings. A self-correction algorithm advances the high mutual coherence, enabling high-signal-to-noise measurements without additional electronics. With its 1 GHz resolution, it excels in monitoring NH$_3$ transitions or NO$_2$ lines at high speeds. This work highlights the complementary strengths of these systems for high-resolution spectroscopy and real time trace gas sensing. △ Less

Submitted 2 September, 2025; originally announced September 2025.

Comments: Authors Eber and Pal contributed equally

Game Theoretic Resilience Recommendation Framework for CyberPhysical Microgrids Using Hypergraph MetaLearning

Authors: S Krishna Niketh, Prasanta K Panigrahi, V Vignesh, Mayukha Pal

Abstract: This paper presents a physics-aware cyberphysical resilience framework for radial microgrids under coordinated cyberattacks. The proposed approach models the attacker through a hypergraph neural network (HGNN) enhanced with model agnostic metalearning (MAML) to rapidly adapt to evolving defense strategies and predict high-impact contingencies. The defender is modeled via a bi-level Stackelberg gam… ▽ More This paper presents a physics-aware cyberphysical resilience framework for radial microgrids under coordinated cyberattacks. The proposed approach models the attacker through a hypergraph neural network (HGNN) enhanced with model agnostic metalearning (MAML) to rapidly adapt to evolving defense strategies and predict high-impact contingencies. The defender is modeled via a bi-level Stackelberg game, where the upper level selects optimal tie-line switching and distributed energy resource (DER) dispatch using an Alternating Direction Method of Multipliers (ADMM) coordinator embedded within the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The framework simultaneously optimizes load served, operational cost, and voltage stability, ensuring all post-defense states satisfy network physics constraints. The methodology is first validated on the IEEE 69-bus distribution test system with 12 DERs, 8 critical loads, and 5 tie-lines, and then extended to higher bus systems including the IEEE 123-bus feeder and a synthetic 300-bus distribution system. Results show that the proposed defense strategy restores nearly full service for 90% of top-ranked attacks, mitigates voltage violations, and identifies Feeder 2 as the principal vulnerability corridor. Actionable operating rules are derived, recommending pre-arming of specific tie-lines to enhance resilience, while higher bus system studies confirm scalability of the framework on the IEEE 123-bus and 300-bus systems. △ Less

Submitted 30 October, 2025; v1 submitted 30 August, 2025; originally announced September 2025.

Discerning and quantifying high frequency activities in EEG under normal and epileptic conditions

Authors: Jyotiraj Nath, Shreya Banerjee, Bhaswati Singha Deo, Mayukha Pal, Prasanta K. Panigrahi

Abstract: We investigate the nature of the modifications in the temporal dynamics manifested in the high-frequency EEG spectra of the normal human brain in comparison to the diseased brain undergoing epilepsy. For this purpose, the Fourier reconstruction is efficaciously made use of after Welch's transform, which helped identify the relevant frequency components undergoing significant changes in the case of… ▽ More We investigate the nature of the modifications in the temporal dynamics manifested in the high-frequency EEG spectra of the normal human brain in comparison to the diseased brain undergoing epilepsy. For this purpose, the Fourier reconstruction is efficaciously made use of after Welch's transform, which helped identify the relevant frequency components undergoing significant changes in the case of epilepsy. The temporal dynamics involved in the EEG signals and their associated variations showed a well-structured periodic pattern characterized by bi-stability and significant quantifiable structural changes during epileptic episodes. In particular, we demonstrate and quantify the precise differences in the high-frequency gamma band (40-100 Hz) present in EEG recordings from neurologically normal participants compared to those with epilepsy. The periodic modulations at two dominant frequencies around 50 Hz and 76 Hz in power spectral density are isolated from high frequency noise through the use of Welch's transform, pinpointing their collective behaviors through a phase-space approach. The reconstructed signals from these restricted frequency domains revealed oscillatory motions showing a bi-stability and bi-furcations with distinct differences between normal and seizure conditions. These differences in the phase space images, when analyzed through linear regression and SVM-based machine learning models, support a classification accuracy of around 94-95% between healthy and ictal states using a publicly available EEG dataset from the University of Bonn (Germany). The partial reconstruction of the dynamics as compared to the earlier studies of the full phase space accurately pinpointed the destabilization of the collective high-frequency synchronous behavior and their precise differences in the normal and diseased conditions, avoiding the other chaotic components of the EEG signals. △ Less

Submitted 18 August, 2025; originally announced August 2025.

Fed-Meta-Align: A Similarity-Aware Aggregation and Personalization Pipeline for Federated TinyML on Heterogeneous Data

Authors: Hemanth Macharla, Mayukha Pal

Abstract: Real-time fault classification in resource-constrained Internet of Things (IoT) devices is critical for industrial safety, yet training robust models in such heterogeneous environments remains a significant challenge. Standard Federated Learning (FL) often fails in the presence of non-IID data, leading to model divergence. This paper introduces Fed-Meta-Align, a novel four-phase framework designed… ▽ More Real-time fault classification in resource-constrained Internet of Things (IoT) devices is critical for industrial safety, yet training robust models in such heterogeneous environments remains a significant challenge. Standard Federated Learning (FL) often fails in the presence of non-IID data, leading to model divergence. This paper introduces Fed-Meta-Align, a novel four-phase framework designed to overcome these limitations through a sophisticated initialization and training pipeline. Our process begins by training a foundational model on a general public dataset to establish a competent starting point. This model then undergoes a serial meta-initialization phase, where it sequentially trains on a subset of IOT Device data to learn a heterogeneity-aware initialization that is already situated in a favorable region of the loss landscape. This informed model is subsequently refined in a parallel FL phase, which utilizes a dual-criterion aggregation mechanism that weights for IOT devices updates based on both local performance and cosine similarity alignment. Finally, an on-device personalization phase adapts the converged global model into a specialized expert for each IOT Device. Comprehensive experiments demonstrate that Fed-Meta-Align achieves an average test accuracy of 91.27% across heterogeneous IOT devices, outperforming personalized FedAvg and FedProx by up to 3.87% and 3.37% on electrical and mechanical fault datasets, respectively. This multi-stage approach of sequenced initialization and adaptive aggregation provides a robust pathway for deploying high-performance intelligence on diverse TinyML networks. △ Less

Submitted 15 August, 2025; originally announced August 2025.

Agentic-AI based Mathematical Framework for Commercialization of Energy Resilience in Electrical Distribution System Planning and Operation

Authors: Aniket Johri, Divyanshi Dwivedi, Mayukha Pal

Abstract: The increasing vulnerability of electrical distribution systems to extreme weather events and cyber threats necessitates the development of economically viable frameworks for resilience enhancement. While existing approaches focus primarily on technical resilience metrics and enhancement strategies, there remains a significant gap in establishing market-driven mechanisms that can effectively comme… ▽ More The increasing vulnerability of electrical distribution systems to extreme weather events and cyber threats necessitates the development of economically viable frameworks for resilience enhancement. While existing approaches focus primarily on technical resilience metrics and enhancement strategies, there remains a significant gap in establishing market-driven mechanisms that can effectively commercialize resilience features while optimizing their deployment through intelligent decision-making. Moreover, traditional optimization approaches for distribution network reconfiguration often fail to dynamically adapt to both normal and emergency conditions. This paper introduces a novel framework integrating dual-agent Proximal Policy Optimization (PPO) with market-based mechanisms, achieving an average resilience score of 0.85 0.08 over 10 test episodes. The proposed architecture leverages a dual-agent PPO scheme, where a strategic agent selects optimal DER-driven switching configurations, while a tactical agent fine-tunes individual switch states and grid preferences under budget and weather constraints. These agents interact within a custom-built dynamic simulation environment that models stochastic calamity events, budget limits, and resilience-cost trade-offs. A comprehensive reward function is designed that balances resilience enhancement objectives with market profitability (with up to 200x reward incentives, resulting in 85% of actions during calamity steps selecting configurations with 4 DERs), incorporating factors such as load recovery speed, system robustness, and customer satisfaction. Over 10 test episodes, the framework achieved a benefit-cost ratio of 0.12 0.01, demonstrating sustainable market incentives for resilience investment. This framework creates sustainable market incentives △ Less

Submitted 6 August, 2025; originally announced August 2025.

Contextual Graph Transformer: A Small Language Model for Enhanced Engineering Document Information Extraction

Authors: Karan Reddy, Mayukha Pal

Abstract: Standard transformer-based language models, while powerful for general text, often struggle with the fine-grained syntax and entity relationships in complex technical, engineering documents. To address this, we propose the Contextual Graph Transformer (CGT), a hybrid neural architecture that combines Graph Neural Networks (GNNs) and Transformers for domain-specific question answering. CGT construc… ▽ More Standard transformer-based language models, while powerful for general text, often struggle with the fine-grained syntax and entity relationships in complex technical, engineering documents. To address this, we propose the Contextual Graph Transformer (CGT), a hybrid neural architecture that combines Graph Neural Networks (GNNs) and Transformers for domain-specific question answering. CGT constructs a dynamic graph over input tokens using sequential, skip-gram, and semantic similarity edges, which is processed by GATv2Conv layers for local structure learning. These enriched embeddings are then passed to a Transformer encoder to capture global dependencies. Unlike generic large models, technical domains often require specialized language models with stronger contextualization and structure awareness. CGT offers a parameter-efficient solution for such use cases. Integrated into a Retrieval-Augmented Generation (RAG) pipeline, CGT outperforms baselines like GPT-2 and BERT, achieving 24.7% higher accuracy than GPT-2 with 62.4% fewer parameters. This gain stems from CGTs ability to jointly model structural token interactions and long-range semantic coherence. The model is trained from scratch using a two-phase approach: pretraining on general text followed by fine-tuning on domain-specific manuals. This highlights CGTs adaptability to technical language, enabling better grounding, entity tracking, and retrieval-augmented responses in real-world applications. △ Less

Submitted 4 August, 2025; originally announced August 2025.

Causality and Interpretability for Electrical Distribution System faults

Authors: Karthik Peddi, Sai Ram Aditya Parisineni, Hemanth Macharla, Mayukha Pal

Abstract: Causal analysis helps us understand variables that are responsible for system failures. This improves fault detection and makes system more reliable. In this work, we present a new method that combines causal inference with machine learning to classify faults in electrical distribution systems (EDS) using graph-based models. We first build causal graphs using transfer entropy (TE). Each fault case… ▽ More Causal analysis helps us understand variables that are responsible for system failures. This improves fault detection and makes system more reliable. In this work, we present a new method that combines causal inference with machine learning to classify faults in electrical distribution systems (EDS) using graph-based models. We first build causal graphs using transfer entropy (TE). Each fault case is represented as a graph, where the nodes are features such as voltage and current, and the edges demonstrate how these features influence each other. Then, the graphs are classified using machine learning and GraphSAGE where the model learns from both the node values and the structure of the graph to predict the type of fault. To make the predictions understandable, we further developed an integrated approach using GNNExplainer and Captums Integrated Gradients to highlight the nodes (features) that influences the most on the final prediction. This gives us clear insights into the possible causes of the fault. Our experiments show high accuracy: 99.44% on the EDS fault dataset, which is better than state of art models. By combining causal graphs with machine learning, our method not only predicts faults accurately but also helps understand their root causes. This makes it a strong and practical tool for improving system reliability. △ Less

Submitted 4 August, 2025; originally announced August 2025.

Lightweight Transformer-Driven Segmentation of Hotspots and Snail Trails in Solar PV Thermal Imagery

Authors: Deepak Joshi, Mayukha Pal

Abstract: Accurate detection of defects such as hotspots and snail trails in photovoltaic modules is essential for maintaining energy efficiency and system reliablility. This work presents a supervised deep learning framework for segmenting thermal infrared images of PV panels, using a dataset of 277 aerial thermographic images captured by zenmuse XT infrared camera mounted on a DJI Matrice 100 drone. The p… ▽ More Accurate detection of defects such as hotspots and snail trails in photovoltaic modules is essential for maintaining energy efficiency and system reliablility. This work presents a supervised deep learning framework for segmenting thermal infrared images of PV panels, using a dataset of 277 aerial thermographic images captured by zenmuse XT infrared camera mounted on a DJI Matrice 100 drone. The preprocessing pipeline includes image resizing, CLAHE based contrast enhancement, denoising, and normalisation. A lightweight semantic segmentation model based on SegFormer is developed, featuring a customised Transformwer encoder and streamlined decoder, and fine-tuned on annotated images with manually labeled defect regions. To evaluate performance, we benchmark our model against U-Net, DeepLabV3, PSPNet, and Mask2Former using consistent preprocessing and augmentation. Evaluation metrices includes per-class Dice score, F1-score, Cohen's kappa, mean IoU, and pixel accuracy. The SegFormer-based model outperforms baselines in accuracy and efficiency, particularly for segmenting small and irregular defects. Its lightweight design real-time deployment on edge devices and seamless integration with drone-based systems for automated inspection of large-scale solar farms. △ Less

Submitted 28 July, 2025; originally announced July 2025.

Comments: 31 pages, 6 figures

An Explainable Equity-Aware P2P Energy Trading Framework for Socio-Economically Diverse Microgrid

Authors: Abhijan Theja, Mayukha Pal

Abstract: Fair and dynamic energy allocation in community microgrids remains a critical challenge, particularly when serving socio-economically diverse participants. Static optimization and cost-sharing methods often fail to adapt to evolving inequities, leading to participant dissatisfaction and unsustainable cooperation. This paper proposes a novel framework that integrates multi-objective mixed-integer l… ▽ More Fair and dynamic energy allocation in community microgrids remains a critical challenge, particularly when serving socio-economically diverse participants. Static optimization and cost-sharing methods often fail to adapt to evolving inequities, leading to participant dissatisfaction and unsustainable cooperation. This paper proposes a novel framework that integrates multi-objective mixed-integer linear programming (MILP), cooperative game theory, and a dynamic equity-adjustment mechanism driven by reinforcement learning (RL). At its core, the framework utilizes a bi-level optimization model grounded in Equity-regarding Welfare Maximization (EqWM) principles, which incorporate Rawlsian fairness to prioritize the welfare of the least advantaged participants. We introduce a Proximal Policy Optimization (PPO) agent that dynamically adjusts socio-economic weights in the optimization objective based on observed inequities in cost and renewable energy access. This RL-powered feedback loop enables the system to learn and adapt, continuously striving for a more equitable state. To ensure transparency, Explainable AI (XAI) is used to interpret the benefit allocations derived from a weighted Shapley value. Validated across six realistic scenarios, the framework demonstrates peak demand reductions of up to 72.6%, and significant cooperative gains. The adaptive RL mechanism further reduces the Gini coefficient over time, showcasing a pathway to truly sustainable and fair energy communities. △ Less

Submitted 24 July, 2025; originally announced July 2025.

OntoRAG: Enhancing Question-Answering through Automated Ontology Derivation from Unstructured Knowledge Bases

Authors: Yash Tiwari, Owais Ahmad Lone, Mayukha Pal

Abstract: Ontologies are pivotal for structuring knowledge bases to enhance question answering (QA) systems powered by Large Language Models (LLMs). However, traditional ontology creation relies on manual efforts by domain experts, a process that is time intensive, error prone, and impractical for large, dynamic knowledge domains. This paper introduces OntoRAG, an automated pipeline designed to derive ontol… ▽ More Ontologies are pivotal for structuring knowledge bases to enhance question answering (QA) systems powered by Large Language Models (LLMs). However, traditional ontology creation relies on manual efforts by domain experts, a process that is time intensive, error prone, and impractical for large, dynamic knowledge domains. This paper introduces OntoRAG, an automated pipeline designed to derive ontologies from unstructured knowledge bases, with a focus on electrical relay documents. OntoRAG integrates advanced techniques, including web scraping, PDF parsing, hybrid chunking, information extraction, knowledge graph construction, and ontology creation, to transform unstructured data into a queryable ontology. By leveraging LLMs and graph based methods, OntoRAG enhances global sensemaking capabilities, outperforming conventional Retrieval Augmented Generation (RAG) and GraphRAG approaches in comprehensiveness and diversity. Experimental results demonstrate OntoRAGs effectiveness, achieving a comprehensiveness win rate of 85% against vector RAG and 75% against GraphRAGs best configuration. This work addresses the critical challenge of automating ontology creation, advancing the vision of the semantic web. △ Less

Submitted 31 May, 2025; originally announced June 2025.

Rydberg Atoms in a Ladder Geometry: Quench Dynamics and Floquet Engineering

Authors: Mainak Pal, Tista Banerjee

Abstract: Rydberg atom quantum simulator platforms are novel quantum simulators for physical systems ranging from condensed matter to particle physics. In this paper, we study out-of-equilibrium quantum dynamics in a model of Rydberg atoms arranged in ladder geometries, with a semi-staggered detuning profile. As the staggering strength ($Δ) $ is varied from $0\rightarrow\infty$, the model exhibits a wide cl… ▽ More Rydberg atom quantum simulator platforms are novel quantum simulators for physical systems ranging from condensed matter to particle physics. In this paper, we study out-of-equilibrium quantum dynamics in a model of Rydberg atoms arranged in ladder geometries, with a semi-staggered detuning profile. As the staggering strength ($Δ) $ is varied from $0\rightarrow\infty$, the model exhibits a wide class of dynamical phenomena, ranging from quantum many-body scars (QMBS) ($Δ\sim 0,1$) to integrability induced slow dynamics and approximate Krylov fractures ($Δ\ge 2$). We study the robustness of these dynamical features against inevitable influences from the environment in the form of pure dephasing and the finite lifetime of the Rydberg excited state. Additionally, by leveraging an underlying spectral reflection symmetry, we design Floquet protocols having dynamical signatures reminiscent of discrete-time-crystalline (DTC) order and exact Floquet flat bands, and study their stability under protocol imperfections. Finally we consider long-range van der Waals interactions and investigate the validity of the kinetic constraints in an out-of-equilibrium scenario. △ Less

Submitted 14 March, 2026; v1 submitted 21 April, 2025; originally announced April 2025.

Comments: Restructuring of the manuscript, updated title

Journal ref: Phys. Rev. B 113, 094306, 12 March, 2026

Monomial retracts of polynomial rings are polynomial rings

Authors: Sagnik Chakraborty, Madhuparna Pal

Abstract: Let $R$ be a ring and $B = R[X_1, \dots, X_n]$ the polynomial ring in $n$ variables over $R$. In this article, we consider retractions $\varphi : B \longrightarrow B$ such that $\varphi(X_i)$ is either a monic monomial or $0$. We prove that if $R$ is an integral domain, then any such retract is isomorphic to $R^{[p]}$, the polynomial ring in $p$ variables over $R$, for some $0 \le p \le n$. We als… ▽ More Let $R$ be a ring and $B = R[X_1, \dots, X_n]$ the polynomial ring in $n$ variables over $R$. In this article, we consider retractions $\varphi : B \longrightarrow B$ such that $\varphi(X_i)$ is either a monic monomial or $0$. We prove that if $R$ is an integral domain, then any such retract is isomorphic to $R^{[p]}$, the polynomial ring in $p$ variables over $R$, for some $0 \le p \le n$. We also characterize different monomial retractions of $B$ which give the same retract. △ Less

Submitted 19 April, 2025; originally announced April 2025.

MSC Class: 13B25; 14A05

Methodology for Detecting Energy Anomalies due to Multi-Replay Attacks on Electric Vehicle Charging Infrastructure

Authors: Sagar Babu Mitikiri, Vedantham Lakshmi Srinivas, Mayukha Pal

Abstract: The increasing deployment of Electric Vehicle Charging Infrastructures (EVCIs) introduces cybersecurity challenges, particularly due to inherent vulnerabilities, making them susceptible to cyberattacks. The vulnerable points in EVCI are charging ports, which serve as the links between the EVs and the EVCI as they transfer the data along with the power. Data spoofing attacks targeting these ports c… ▽ More The increasing deployment of Electric Vehicle Charging Infrastructures (EVCIs) introduces cybersecurity challenges, particularly due to inherent vulnerabilities, making them susceptible to cyberattacks. The vulnerable points in EVCI are charging ports, which serve as the links between the EVs and the EVCI as they transfer the data along with the power. Data spoofing attacks targeting these ports can compromise security, reliability, and overall system performance by introducing anomalies in operational data. An efficient method for identifying the charging port current magnitude variations is presented in this research. The MATLAB/SIMULINK environment simulates an EVCI system for various data generating scenarios. A Temporal Convolution Network - Autoencoder (TCN-AE) model is used in training the multivariate time series data of EVCI and reconstructing it. The abnormalities in data are that various charging port current magnitudes are replaced with their respective data of different durations, thus enabling the replay attack scenarios. To detect anomalies, the error between the original and reconstructed data is computed, and these error values are used for detecting the anomalies. With the help of the mean vector and covariance matrices of the errors, the anomaly score is computed in the form of Mahalanobis distance. The threshold is obtained from the short sub-sequence of the errors and optimized for the whole time series data. The obtained optimal threshold is compared with the anomaly score to detect the anomaly. The model demonstrates robust performance in data reconstruction by identifying anomalies with an accuracy of 99.64%, to enhance the reliability and security in operations of EVCI. △ Less

Submitted 31 March, 2025; originally announced April 2025.

Measurement of Kaon Directed Flow in Au+Au Collisions in the High Baryon Density Region

Authors: STAR Collaboration, B. E. Aboona, J. Adam, G. Agakishiev, I. Aggarwal, M. M. Aggarwal, Z. Ahammed, A. Aitbayev, I. Alekseev, E. Alpatov, A. K. Alshammri, A. Aparin, S. Aslam, J. Atchison, G. S. Averichev, V. Bairathi, X. Bao, P. Barik, K. Barish, S. Behera, P. Bhagat, A. Bhasin, S. Bhatta, I. G. Bordyuzhin, J. D. Brandenburg , et al. (363 additional authors not shown)

Abstract: Rapidity-odd directed flow $v_1$ measurements are presented for $K^{\pm}$ and $K^0_S$ in Au$+$Au collisions at $\sqrt{s_{\text{NN}}}$ = 3.0, 3.2, 3.5, and 3.9 GeV with the STAR experiment. For comparison, $v_1$ of $π^{\pm}$, protons, and $Λ$ from the same collisions are also discussed. The mid-rapidity $v_1$ slope $\text{d}v_1/\text{d}y|_{y=0}$ for protons and $Λ$ is positive in these collisions.… ▽ More Rapidity-odd directed flow $v_1$ measurements are presented for $K^{\pm}$ and $K^0_S$ in Au$+$Au collisions at $\sqrt{s_{\text{NN}}}$ = 3.0, 3.2, 3.5, and 3.9 GeV with the STAR experiment. For comparison, $v_1$ of $π^{\pm}$, protons, and $Λ$ from the same collisions are also discussed. The mid-rapidity $v_1$ slope $\text{d}v_1/\text{d}y|_{y=0}$ for protons and $Λ$ is positive in these collisions. On the other hand, $v_1$ slope of kaons exhibits a strong $p_\text{T}$ dependence: negative at $p_\text{T} <$ 0.6 GeV/$c$ and positive at higher $p_\text{T}$. A similar $p_\text{T}$ dependence is also evident for the $v_1$ slope of charged pions. Compared to the spectator-removed calculations in Au$+$Au collisions at $\sqrt{s_{\text{NN}}} =$ 3.0-3.9 GeV, the JAM model demonstrates a pronounced shift of the $v_1$ slopes of mesons towards the negative direction. It suggests that the shadowing effect of the spectators plays an important role in the observed kaon anti-flow at low $p_\text{T}$ in the high baryon density region of non-central collisions. △ Less

Submitted 31 May, 2026; v1 submitted 30 March, 2025; originally announced March 2025.

Comments: 8 pages, 5 figures

Journal ref: Phys. Lett. B 875, 140353 (2026)

Physics-Informed Neural Network-Based Control for Grid-Forming Converter's Stability Under Overload Conditions

Authors: Abhay Kumar, Dushyant Sharma, Mayukha Pal

Abstract: Grid-forming converters (GFCs) are crucial for frequency and voltage stability in modern power systems. However, their performance under overload conditions remains a challenge. This paper highlights the limitations of existing approaches in managing DC source saturation and AC current limits, emphasizing the need for improved control strategies to ensure system stability. This paper proposes a co… ▽ More Grid-forming converters (GFCs) are crucial for frequency and voltage stability in modern power systems. However, their performance under overload conditions remains a challenge. This paper highlights the limitations of existing approaches in managing DC source saturation and AC current limits, emphasizing the need for improved control strategies to ensure system stability. This paper proposes a control strategy based on a physics-informed neural network (PINN) to improve GFC performance under overloaded conditions, effectively preventing switch failures and mitigating DC source saturation. This approach outperforms conventional methods by maintaining stable voltage and frequency, even under significant load increase where traditional droop control alone proves inadequate. The post-disturbance operating point of GFCs remains unchanged using PINN-based control with an improvement of 0.245 Hz in frequency and 0.03 p.u. in active power when compared to an already existing current limitation strategy. Additionally, it reduces peak voltage deviations during transients by 24.14\%, lowers the rate of change of frequency (ROCOF) from 0.02 Hz/s to 0.005 Hz/s, and improves the rate of change of voltage (ROCOV), keeping both within acceptable limits. These improvements significantly enhance system resilience, especially in inertia-less power networks. △ Less

Submitted 22 May, 2025; v1 submitted 27 March, 2025; originally announced March 2025.

Permutation polynomials over finite fields from low-degree rational functions

Authors: Kirpa Garg, Sartaj Ul Hasan, Chunlei Li, Hridesh Kumar, Mohit Pal

Abstract: This paper considers permutation polynomials over the finite field $F_{q^2}$ in even characteristic by utilizing low-degree permutation rational functions over $F_q$. As a result, we obtain two classes of permutation binomials and six classes of permutation pentanomials over $F_{q^2}$. Additionally, we show that the obtained binomials and pentanomials are quasi-multiplicative inequivalent to the k… ▽ More This paper considers permutation polynomials over the finite field $F_{q^2}$ in even characteristic by utilizing low-degree permutation rational functions over $F_q$. As a result, we obtain two classes of permutation binomials and six classes of permutation pentanomials over $F_{q^2}$. Additionally, we show that the obtained binomials and pentanomials are quasi-multiplicative inequivalent to the known ones in the literature. △ Less

Submitted 21 August, 2025; v1 submitted 26 March, 2025; originally announced March 2025.

Comments: 32 pages

MSC Class: 11T06; 12E20

Inhomogeneity, Fluctuations, and Gap Filling in Overdoped Cuprates

Authors: Miguel Antonio Sulangi, Willem Farmilo, Andreas Kreisel, Mainak Pal, W. A. Atkinson, P. J. Hirschfeld

Abstract: Several recent experiments have challenged the premise that cuprate high-temperature superconductors approach conventional Landau-BCS behavior in the high-doping limit. We argue, based on an analysis of their superconducting spectra, that anomalous properties seen in the most-studied overdoped cuprates require a pairing interaction that is strongly inhomogeneous on nm length scales. This is consis… ▽ More Several recent experiments have challenged the premise that cuprate high-temperature superconductors approach conventional Landau-BCS behavior in the high-doping limit. We argue, based on an analysis of their superconducting spectra, that anomalous properties seen in the most-studied overdoped cuprates require a pairing interaction that is strongly inhomogeneous on nm length scales. This is consistent with recent proposals that the "strange-metal" phase above $T_c$ in the same doping range arises from a spatially random interaction. We show, via mean-field Bogoliubov-de Gennes (BdG) calculations and time-dependent Ginzburg-Landau (TDGL) simulations, that key features of the observed tunneling spectra are reproduced when both inhomogeneity and thermal phase fluctuations are accounted for. In accord with experiments, BdG calculations find that low-$T$ spectra are highly inhomogeneous and exhibit a low-energy spectral shoulder and broad coherence peaks. However, the spectral gap in this approach becomes homogeneous at high $T$, in contrast to experiments. This is resolved when thermal fluctuations are included; in this case, global phase coherence is lost at the superconducting $T_c$ via a broadened BKT transition, while robust phase-coherent superconducting islands persist well above $T_c$. The local spectrum remains inhomogeneous at $T_c$, and the gap is found to fill instead of close with increasing temperature. △ Less

Submitted 23 November, 2025; v1 submitted 26 March, 2025; originally announced March 2025.

Comments: 12 pages, 7 figures, published version, includes minor changes

Journal ref: Phys. Rev. Res. 7, 043201 (2025)

Measurement of $β$-particles to determine cross sections relevant to the weak r-process

Authors: Sándor R. Kovács, Tibor Norbert Szegedi, Ákos Tóth, Attila Németh, Manoj Kumar Pal, Edit Szilágyi, Mihály Braun, György Gyürky, Zoltán Elekes, Zoltán Halász, Tamás Szücs, Gábor Gyula Kiss

Abstract: The neutron-rich isotopes with 30 $\leq$ Z $\leq$ 45 are thought to be synthesised in neutrino-driven winds after the collapse of a massive star. This nucleosynthesis scenario, called the weak r-process, is studied using nuclear reaction network calculations. The accuracy of the nucleosynthesis simulations is strongly influenced by the reliability of the nuclear physics input parameters used. Rece… ▽ More The neutron-rich isotopes with 30 $\leq$ Z $\leq$ 45 are thought to be synthesised in neutrino-driven winds after the collapse of a massive star. This nucleosynthesis scenario, called the weak r-process, is studied using nuclear reaction network calculations. The accuracy of the nucleosynthesis simulations is strongly influenced by the reliability of the nuclear physics input parameters used. Recently, it has been demonstrated that ($α$,n) reactions play a particularly important role in the weak r-process, but their rates -- computed from the cross sections -- are only known with large uncertainties in the astrophysically relevant temperature range. The half-lives of the products of some key reactions are such that, in principle, the cross sections can be studied using the activation technique. In many cases, however, the $β$-decay of the reaction products leads to the ground state of the daughter nucleus, hence no gamma emission occurs. The purpose of this manuscript is to present our setup with which we determine the cross sections by measuring the yield of $β$-particles emitted during radioactive decay. The $^{86}$Kr($α$,n)$^{89}$Sr reaction cross-section measurement is used, as a case study. △ Less

Submitted 25 March, 2025; originally announced March 2025.

Comments: Accepted for publication in NIMA

Journal ref: Nuclear Instruments and Methods in Physics Research Section A 1073, 170272 (2025)

Bright quantum dot light sources using monolithic microlenses on gold back-reflectors

Authors: Moritz Langer, Sai A. Dhurjati, Yared G. Zena, Ahmad Rahimi, Mandira Pal, Liesa Raith, Sandra Nestler, Riccardo Bassoli, Frank H. P. Fitzek, Oliver G. Schmidt, Caspar Hopfmann

Abstract: We present the fabrication process of bright $GaAs$ quantum dot (QD) photon sources by non-deterministic embedding into broadband monolithic $Al_{0.15}Ga_{0.85}As$ microlens arrays on gold-coated substrates. Arrays of cylindrical photoresist templates, with diameters ranging from $2$ $μm$ to $5$ $μm$, are thermally reflowed and subsequently transferred into the $Al_{0.15}Ga_{0.85}As$ thin-film sem… ▽ More We present the fabrication process of bright $GaAs$ quantum dot (QD) photon sources by non-deterministic embedding into broadband monolithic $Al_{0.15}Ga_{0.85}As$ microlens arrays on gold-coated substrates. Arrays of cylindrical photoresist templates, with diameters ranging from $2$ $μm$ to $5$ $μm$, are thermally reflowed and subsequently transferred into the $Al_{0.15}Ga_{0.85}As$ thin-film semiconductor heterostructure with embedded quantum dots through an optimized anisotropic and three-dimensional shape-preserving reactive ion etching process. This methodology facilitated the fabrication of large-scale ($2$ $mm$ $\times$ $4$ $mm$) and densely packed arrays of uniformly shaped microlenses ($\sim$ $40 \times 10^3$ $mm^{-1}$), with the brightest emissions from QDs embedded in microlenses exhibiting lateral diameters and heights of $2.7$ $μm$ and $1.35$ $μm$, respectively. Finite-difference time-domain simulations of both idealized and fabricated lens shapes provide a comprehensive three-dimensional analysis of the device performance and optimization potentials such as anti-reflection coatings. It is found that free-space extraction (fiber-coupled) efficiencies of up to $62$ $\%$ ($37$ $\%$) are achievable for hemispherical QD-microlenses on gold-coated substrates. A statistical model for the fabrication yield of QD-microlenses is developed and experimentally corroborated by photoluminescence spectroscopy of fabricated microlens arrays. This analysis exhibited a free-space intensity enhancement by factors of up to $\times 200$ in approximately $1$ out of $200$ microlenses, showing good agreement to the theoretical expectations. This scalable fabrication strategy underscores the potential of these compact, high-efficiency sources offering new prospects for applications of these devices in future large-scale quantum networks. △ Less

Submitted 10 March, 2025; originally announced March 2025.

Signature of Seyfert-like component in a blazar 3C 273 and its reflection-based explanation

Authors: Haritma Gaur, Main Pal, Muhammad S. Anjum, Kiran Wani, Pankaj Kushwaha, Ashwani Pandey, Liang Chen

Abstract: We present the results of blazar 3C 273 obtained from simultaneous observations obtained using XMM-Newton and NuSTAR satellites during the period 2015-2019 in five epochs. When the spectra are modeled with a power-law, significant residuals arise below 2 keV and in the energy range of 30-78 keV in NuSTAR data. Residuals in the lower energy band represent soft X-ray excess while at higher energies… ▽ More We present the results of blazar 3C 273 obtained from simultaneous observations obtained using XMM-Newton and NuSTAR satellites during the period 2015-2019 in five epochs. When the spectra are modeled with a power-law, significant residuals arise below 2 keV and in the energy range of 30-78 keV in NuSTAR data. Residuals in the lower energy band represent soft X-ray excess while at higher energies it likely represents Compton reflection hump which might be a weak component arising from dense and cold material. The presence of a faint iron line is present in XMM-Newton observations. We interpret such features as attributed to the coronal emission plus those arising from reflection from an accretion disk. We model the SEDs with the single zone inverse Compton jet model based on Synchrotron Self Compton and External Compton phenomena. It is found that a one-zone synchrotron plus IC model explains quite well the SEDs but the jet component alone fails to fit the multiband X-ray emission for the low state of this object in 2018 and 2019 which arises due to spectral flattening at low energy X-rays, indicating that an additional Seyfert-like thermal component must be present at X-rays. This is further supported by a big blue bump present in the optical/ultraviolet band in all SEDs. Finally, we analyzed all the epochs using relxill model to incorporate relativistic reflection to model those residuals of soft excess and Compton hump in the X-ray bands. △ Less

Submitted 18 February, 2025; originally announced February 2025.

Comments: 12 pages and 4 figures. Accepted for publication in ApJ

An ultra-compact deterministic source of maximally entangled photon pairs

Authors: M. Langer, P. Ruchka, A. Rahimi, S. Jakovljevic, Y. G. Zena, A. Danilov, M. Pal, R. Bassoli, F. H. P. Fitzek, O. G. Schmidt, H. Giessen, C. Hopfmann

Abstract: We present an ultra-compact source of maximally entangled on-demand photon pairs. Our results are based on coupling of single GaAs quantum dots that are embedded in monolithic micro-lenses to a single-mode fiber with directly attached to 3D-printed micro-optics (NA of 0.6) inside a cryogenic environment. This approach, which is geared towards future integration into industrial environments, yields… ▽ More We present an ultra-compact source of maximally entangled on-demand photon pairs. Our results are based on coupling of single GaAs quantum dots that are embedded in monolithic micro-lenses to a single-mode fiber with directly attached to 3D-printed micro-optics (NA of 0.6) inside a cryogenic environment. This approach, which is geared towards future integration into industrial environments, yields state-of-the-art entangled photon pair creation performance while retaining flexibility and adjustability required for long-term operation of such a device - all while dramatically reducing the overall system footprint. We demonstrate near diffraction-limited performance and hyperspectral imaging utilizing a 3D-printed micro-objective with a full width at half maximum resolution limit of 604(16) nm when operating the system at a cryogenic temperature of 3.8 K. Furthermore, we prove that this system can be used to achieve single photon emission rates of 392(20) kHz at a 76 MHz pump rate and purities of 99.2(5) % using two-photon resonant excitation. Utilizing the exciton-biexciton emission cascade available in GaAs quantum dots under resonant excitation, near maximally entangled photon pairs with peak entanglement negatives 2n of 0.96(2) in a 4 ps time window, and 0.81(1) when averaged over one exciton lifetime, are demonstrated. △ Less

Submitted 11 March, 2025; v1 submitted 17 February, 2025; originally announced February 2025.

Comments: 14 pages, 13 figures

Integrated Optimization and Game Theory Framework for Fair Cost Allocation in Community Microgrids

Authors: K. Victor Sam Moses Babu, Pratyush Chakraborty, Mayukha Pal

Abstract: Fair cost allocation in community microgrids remains a significant challenge due to the complex interactions between multiple participants with varying load profiles, distributed energy resources, and storage systems. Traditional cost allocation methods often fail to adequately address the dynamic nature of participant contributions and benefits, leading to inequitable distribution of costs and re… ▽ More Fair cost allocation in community microgrids remains a significant challenge due to the complex interactions between multiple participants with varying load profiles, distributed energy resources, and storage systems. Traditional cost allocation methods often fail to adequately address the dynamic nature of participant contributions and benefits, leading to inequitable distribution of costs and reduced participant satisfaction. This paper presents a novel framework integrating multi-objective optimization with cooperative game theory for fair and efficient microgrid operation and cost allocation. The proposed approach combines mixed-integer linear programming for optimal resource dispatch with Shapley value analysis for equitable benefit distribution, ensuring both system efficiency and participant satisfaction. The framework was validated using real-world data across six distinct operational scenarios, demonstrating significant improvements in both technical and economic performance. Results show peak demand reductions ranging from 7.8% to 62.6%, solar utilization rates reaching 114.8% through effective storage integration, and cooperative gains of up to $1,801.01 per day. The Shapley value-based allocation achieved balanced benefit-cost distributions, with net positions ranging from -16.0% to +14.2% across different load categories, ensuring sustainable participant cooperation. △ Less

Submitted 12 February, 2025; originally announced February 2025.

Demand Response Optimization MILP Framework for Microgrids with DERs

Authors: K. Victor Sam Moses Babu, Pratyush Chakraborty, Mayukha Pal

Abstract: The integration of renewable energy sources in microgrids introduces significant operational challenges due to their intermittent nature and the mismatch between generation and demand patterns. Effective demand response (DR) strategies are crucial for maintaining system stability and economic efficiency, particularly in microgrids with high renewable penetration. This paper presents a comprehensiv… ▽ More The integration of renewable energy sources in microgrids introduces significant operational challenges due to their intermittent nature and the mismatch between generation and demand patterns. Effective demand response (DR) strategies are crucial for maintaining system stability and economic efficiency, particularly in microgrids with high renewable penetration. This paper presents a comprehensive mixed-integer linear programming (MILP) framework for optimizing DR operations in a microgrid with solar generation and battery storage systems. The framework incorporates load classification, dynamic price thresholding, and multi-period coordination for optimal DR event scheduling. Analysis across seven distinct operational scenarios demonstrates consistent peak load reduction of 10\% while achieving energy cost savings ranging from 13.1\% to 38.0\%. The highest performance was observed in scenarios with high solar generation, where the framework achieved 38.0\% energy cost reduction through optimal coordination of renewable resources and DR actions. The results validate the framework's effectiveness in managing diverse operational challenges while maintaining system stability and economic efficiency. △ Less

Submitted 12 February, 2025; originally announced February 2025.

Enhanced Rapid Detection of High-impedance Arc Faults in Medium Voltage Electrical Distribution Networks

Authors: Kriti Thakur, Divyanshi Dwivedi, K. Victor Sam Moses Babu, Alivelu Manga Parimi, Prasanta K. Panigrahi, Pradeep Kumar Yemula, Pratyush Chakraborty, Mayukha Pal

Abstract: High-impedance arc faults in AC power systems have the potential to lead to catastrophic accidents. However, significant challenges exist in identifying these faults because of the much weaker characteristics and variety when grounded with different surfaces. Previous research has concentrated predominantly on arc fault detection in low-voltage systems, leaving a significant gap in medium-voltage… ▽ More High-impedance arc faults in AC power systems have the potential to lead to catastrophic accidents. However, significant challenges exist in identifying these faults because of the much weaker characteristics and variety when grounded with different surfaces. Previous research has concentrated predominantly on arc fault detection in low-voltage systems, leaving a significant gap in medium-voltage applications. In this work, a novel approach has been developed that enables rapid arc fault detection for medium-voltage distribution lines. In contrast to existing black-box feature-based approaches, the Hankel alternative view of the Koopman (HAVOK) analysis developed from nonlinear dynamics has been applied, which not only offers interpretable features but also opens up new application options in the area of arc fault detection. The method achieves a much faster detection speed in 0.45 ms, 99.36\% enhanced compared to harmonic randomness and waveform distortion method, thus making it suitable for real-time applications. It demonstrates the ability to detect arc faults across various scenarios, including different grounding surfaces and levels of system noise, boosting its practical importance for stakeholders in safety-critical industries. △ Less

Submitted 23 June, 2025; v1 submitted 9 February, 2025; originally announced February 2025.

A Comprehensive Metric for Resilience Evaluation of Power Distribution Systems under Cyber Attacks

Authors: Mitikiri Sagar Babu, Victor Sam Moses Babu, Vedantham Lakshmi Srinivas, Pratyush Chakraborty, Mayukha Pal

Abstract: Power distribution systems (PDS) serve as the backbone of our modern society, ensuring electricity reaches homes, businesses, and critical infrastructure. However, the increasing digitization and interconnectivity of these systems have exposed them to cyber threats. This study presents a comprehensive approach to evaluate and enhance the resilience of PDS under cyber attacks using the Common Vulne… ▽ More Power distribution systems (PDS) serve as the backbone of our modern society, ensuring electricity reaches homes, businesses, and critical infrastructure. However, the increasing digitization and interconnectivity of these systems have exposed them to cyber threats. This study presents a comprehensive approach to evaluate and enhance the resilience of PDS under cyber attacks using the Common Vulnerability Scoring System (CVSS) and complex network parameters. By systematically assessing vulnerabilities and computing resilience once critical CVSS thresholds are reached, this work identifies key resilience metrics including the critical loads service requirements. The proposed methodology improves system resilience through strategic tie-line switching, which is validated on the modified IEEE 33-bus system. Four case studies are conducted, illustrating the performance of the proposed methodology under various cyber attack scenarios. The results demonstrate the effectiveness of the approach in quantifying and enhancing resilience, offering a valuable tool for PDS operators to mitigate risks and ensure continuous service delivery to critical loads during the exploitation of cyber threats. △ Less

Submitted 21 January, 2025; originally announced January 2025.

Ultra-resolution photochemical sensing

Authors: Lukas Fuerst, Alexander Eber, Mithun Pal, Emily Hruska, Clemens Hofmann, Iouli Gordon, Martin Schultze, Rolf Breinbauer, Birgitta Bernhardt

Abstract: Photochemistry in the earth's atmosphere is driven by the sun, continuously altering the concentration and spatial distribution of pollutants. Precisely monitoring their atmospheric abundance relies predominantly on optical sensing, which requires the knowledge of exact absorption cross sections. One key pollutant which impacts many photochemical reaction-pathways is formaldehyde. Agreement on for… ▽ More Photochemistry in the earth's atmosphere is driven by the sun, continuously altering the concentration and spatial distribution of pollutants. Precisely monitoring their atmospheric abundance relies predominantly on optical sensing, which requires the knowledge of exact absorption cross sections. One key pollutant which impacts many photochemical reaction-pathways is formaldehyde. Agreement on formaldehyde absolute absorption cross section remains elusive in the photochemically-relevant ultraviolet spectral region, hampering sensitive concentration tracking. Here, we introduce free-running ultraviolet dual comb spectroscopy, combining high spectral resolution (1 GHz), broad spectral coverage (12 THz), and fast acquisition speed (500 ms), as a novel method for absolute absorption cross section determination with unprecedented fidelity. Within this bandwidth, our method uncovers almost one order of magnitude more rovibrational transitions than detected before which leads to refined rotational constants for high-level quantum simulations of molecular eigenstates. This ultra-resolution method can be generalized to provide a universal tool for fast electronic fingerprinting of atmospherically-relevant species, both for sensing applications and to benchmark improvements of ab-initio quantum theory. △ Less

Submitted 13 January, 2025; originally announced January 2025.

Comments: 19 pages, 8 figures, 1 table

Multi-wavelength observations of a jet launch in real time from the post-changing-look Active Galaxy 1ES 1927+654

Authors: Sibasish Laha, Eileen T. Meyer, Dev R. Sadaula, Ritesh Ghosh, Dhrubojyoti Sengupta, Megan Masterson, Onic I. Shuvo, Matteo Guainazzi, Claudio Ricci, Mitchell C. Begelman, Alexander Philippov, Rostom Mbarek, Amelia M. Hankla, Erin Kara, Francesca Panessa, Ehud Behar, Haocheng Zhang, Fabio Pacucci, Main Pal, Federica Ricci, Ilaria Villani, Susanna Bisogni, Fabio La Franca, Stefano Bianchi, Gabriele Bruni , et al. (12 additional authors not shown)

Abstract: We present results from a high cadence multi-wavelength observational campaign of the enigmatic changing look AGN 1ES 1927+654 from May 2022- April 2024, coincident with an unprecedented radio flare (an increase in flux by a factor of $\sim 60$ over a few months) and the emergence of a spatially resolved jet at $0.1-0.3$ pc scales (Meyer et al. 2024). Companion work has also detected a recurrent q… ▽ More We present results from a high cadence multi-wavelength observational campaign of the enigmatic changing look AGN 1ES 1927+654 from May 2022- April 2024, coincident with an unprecedented radio flare (an increase in flux by a factor of $\sim 60$ over a few months) and the emergence of a spatially resolved jet at $0.1-0.3$ pc scales (Meyer et al. 2024). Companion work has also detected a recurrent quasi-periodic oscillation (QPO) in the $2-10$ keV band with an increasing frequency ($1-2$ mHz) over the same period (Masterson et al., 2025). During this time, the soft X-rays ($0.3-2$ keV) monotonically increased by a factor of $\sim 8$, while the UV emission remained near-steady with $<30\%$ variation and the $2-10$ keV flux showed variation by a factor $\lesssim 2$. The weak variation of the $2-10$ keV X-ray emission and the stability of the UV emission suggest that the magnetic energy density and accretion rate are relatively unchanged, and that the jet could be launched due to a reconfiguration of the magnetic field (toroidal to poloidal) close to the black hole. Advecting poloidal flux onto the event horizon would trigger the Blandford-Znajek (BZ) mechanism, leading to the onset of the jet. The concurrent softening of the coronal slope (from $Γ= 2.70\pm 0.04$ to $Γ=3.27\pm 0.04$), the appearance of a QPO, and low coronal temperature ($kT_{e}=8_{-3}^{+8}$ keV) during the radio outburst suggest that the poloidal field reconfiguration can significantly impact coronal properties and thus influence jet dynamics. These extraordinary findings in real time are crucial for coronal and jet plasma studies, particularly as our results are independent of coronal geometry. △ Less

Submitted 4 January, 2025; originally announced January 2025.

Comments: Submitted to ApJ after minor referee comments

Phase-locked feed forward stabilization for dual comb spectroscopy

Authors: Mithun Pal, Alexander Eber, Lukas Fürst, Emily Hruska, Marcus Ossiander, Birgitta Bernhardt

Abstract: Sustained mutual coherence between two combs over extended periods is a prerequisite for dual-comb spectroscopy (DCS), particularly in achieving high-resolution molecular spectroscopy and precise spectral measurements. However, achieving long coherence times remains a challenge for Yb-doped frequency combs. This work introduces an experimental approach for phase-stable DCS using Yb-doped frequency… ▽ More Sustained mutual coherence between two combs over extended periods is a prerequisite for dual-comb spectroscopy (DCS), particularly in achieving high-resolution molecular spectroscopy and precise spectral measurements. However, achieving long coherence times remains a challenge for Yb-doped frequency combs. This work introduces an experimental approach for phase-stable DCS using Yb-doped frequency combs at 1.03 $μ$m with a novel feed-forward method, combatting the limitations of mutual coherence. Without relying on computer-based phase correction, we achieve a coherence time of 1000 seconds - three orders of magnitude longer than the current state of the art for DCS. This extended coherence enables time-domain averaging, resulting in a signal-to-noise ratio (SNR) of 2045. We demonstrate high-resolution monitoring of weak overtone transitions in the P and R branches of C${_2}$H${_2}$, with good agreement with HITRAN. The phase-locked multiheterodyne system also enables phase spectrum measurements with a scatter down to 7 mrad. Furthermore, we successfully extend our technique to the visible wavelength range using second harmonic generation, achieving high-resolution spectra of NO${_2}$ with excellent SNR. The method offers high-frequency accuracy and demonstrates the potential of Yb-doped systems for multiplexed metrology, effectively extending the capabilities of DCS as a powerful tool for multi-disciplinary applications. △ Less

Submitted 8 December, 2024; originally announced December 2024.

Scar-induced imbalance in staggered Rydberg ladders

Authors: Mainak Pal, Madhumita Sarkar, K. Sengupta, Arnab Sen

Abstract: We demonstrate that the kinematically-constrained model of Rydberg atoms on a two-leg ladder with staggered detuning, $Δ\in [0,1]$, has quantum many-body scars (QMBS) in its spectrum and represents a non-perturbative generalization of the paradigmatic PXP model defined on a chain. We show that these QMBS result in coherent many-body revivals and site-dependent magnetization dynamics for both Néel… ▽ More We demonstrate that the kinematically-constrained model of Rydberg atoms on a two-leg ladder with staggered detuning, $Δ\in [0,1]$, has quantum many-body scars (QMBS) in its spectrum and represents a non-perturbative generalization of the paradigmatic PXP model defined on a chain. We show that these QMBS result in coherent many-body revivals and site-dependent magnetization dynamics for both Néel and Rydberg vacuum initial states around $Δ=1$. The latter feature leads to eigenstate thermalization hypothesis (ETH)-violating finite imbalance at long times in a disorder-free system. This is further demonstrated by constructing appropriate local imbalance operators that display nonzero long-time averages for Néel and vacuum initial states. We also study the fidelity and Shannon entropy for such dynamics which, along with the presence of long-time finite imbalance, brings out the qualitatively different nature of QMBS in PXP ladders with $Δ\sim 1$ from those in the PXP chain. Finally, we identify additional exact mid-spectrum zero modes that stay unchanged as a function of $Δ$ and violate ETH. △ Less

Submitted 2 April, 2025; v1 submitted 4 November, 2024; originally announced November 2024.

Comments: v4; updated Fig. 5; main text+ supplementary material

Journal ref: Phys. Rev. B 111, L161101 (2025)