Showing 1–50 of 183 results for author: Andersen, R

Normative Alignment of Recommender Systems via Internal Label Shift

Authors: Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Ryotaro Shimizu, Julian McAuley, Pierre-Alexandre Mattei, Jes Frellsen

Abstract: We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions over item-level attributes, such as categories. Recommender systems optimized solely for user engagement often fail to satisfy broader normative objectives, including fairness, diversity, and editorial values. NAILS mod… ▽ More We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions over item-level attributes, such as categories. Recommender systems optimized solely for user engagement often fail to satisfy broader normative objectives, including fairness, diversity, and editorial values. NAILS modifies the user-conditional item distribution to induce a specified marginal distribution over attributes while preserving the preferences learned by an existing recommender system and requiring no model retraining. We formulate this problem as a form of label shift applied internally within a hierarchical classification framework. By adopting a stakeholder-centric perspective, NAILS enables recommendation outputs to be aligned with global normative objectives. Empirically, we show that NAILS consistently improves attribute-level alignment with minimal impact on user engagement, providing a practical mechanism for value-driven recommendation. △ Less

Submitted 12 July, 2026; originally announced July 2026.

Comments: 6 pages. Published in the Proceedings of the Nineteenth ACM Conference on Recommender Systems (RecSys '25), Prague, Czech Republic, September 22-26, 2025. Code available at https://github.com/johanneskruse/nails

ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation

Authors: Johannes Kruse, Ryotaro Shimizu, Kasper Lindskow, Jon Tofteskov, Michael Riis Andersen, Julian McAuley, Jes Frellsen

Abstract: We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deployment. ZoRRO outperforms strong neural baselines in offline ranking evaluations and achieves click-through rate performance in online A/B testing that is nearly on par with a state-of-the-art deep learning model, while ope… ▽ More We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deployment. ZoRRO outperforms strong neural baselines in offline ranking evaluations and achieves click-through rate performance in online A/B testing that is nearly on par with a state-of-the-art deep learning model, while operating more than 600 times faster. Our experiments reveal gaps between offline and online performance and demonstrate that models with similar click-through rate outcomes can produce markedly different recommendation distributions, thereby influencing the overall news flow. These findings position ZoRRO as a practical and efficient solution for large-scale news recommendation and highlight the importance of evaluating recommender systems using metrics beyond accuracy alone. △ Less

Submitted 12 July, 2026; originally announced July 2026.

Comments: 6 pages, 2 figures. Accepted at the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '26), Melbourne, Australia, July 20-24, 2026. Code available at https://github.com/johanneskruse/zorro

Optical spectroscopy of Bose-Einstein condensates at finite temperature

Authors: R. M. F. Andersen, L. N. Stokholm, I. Zebergs, N. R. Neubert, A. S. Chatterley, N. Kjærgaard, J. J. Arlt

Abstract: We report on optical spectroscopic measurements of ultracold and partially condensed 87Rb gases, which show distinct spectral features due to the thermal and the Bose condensed components in the frequency domain. These features are detected in-situ by using a dark-field configuration with single-photon sensitivity and a frequency-agile laser system. To interpret the observed spectra, we develop a… ▽ More We report on optical spectroscopic measurements of ultracold and partially condensed 87Rb gases, which show distinct spectral features due to the thermal and the Bose condensed components in the frequency domain. These features are detected in-situ by using a dark-field configuration with single-photon sensitivity and a frequency-agile laser system. To interpret the observed spectra, we develop a model for light propagation through an inhomogeneous atomic cloud. This model enables the extraction of temperature and atom number, which we benchmark against conventional time-of-flight absorption imaging. The spectroscopically obtained cloud parameters show enhanced sensitivity to small thermal fractions in nearly pure condensates. We further compare the spectroscopy in dark-field and bright-field configurations, demonstrating the superior performance of the former. Our results reveal previously unexplored spectral structure in optically dense ultracold gases and establish spectroscopy as a tool for characterizing ultracold systems at very low temperatures. △ Less

Submitted 23 June, 2026; originally announced June 2026.

Comments: 11 pages, 8 figures

Precision Cell Resampling with a Relative and Resonant Aware Metric

Authors: Jeppe R. Andersen, Ella Cole, Andreas Maier

Abstract: We present a metric on the space of scattering events based on relative transverse momenta and with explicit sensitivity to intermediate resonances. With this new metric, negative weights in an event sample can be reduced substantially through cell resampling, while preserving the predicted properties of the resonance with high accuracy. We demonstrate the efficiency on a NLO event sample for the… ▽ More We present a metric on the space of scattering events based on relative transverse momenta and with explicit sensitivity to intermediate resonances. With this new metric, negative weights in an event sample can be reduced substantially through cell resampling, while preserving the predicted properties of the resonance with high accuracy. We demonstrate the efficiency on a NLO event sample for the production of a leptonically decaying W boson together with two jets. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Comments: 13 pages, 5 figures

Report number: IPPP/26/38

Same Graph, Different Likelihoods: Calibration of Autoregressive Graph Generators via Permutation-Equivalent Encodings

Authors: Laurits Fredsgaard, Aaron Thomas, Michael Riis Andersen, Mikkel N. Schmidt, Mahito Sugiyama

Abstract: Autoregressive graph generators define likelihoods via a sequential construction process, but these likelihoods are only meaningful if they are consistent across all linearizations of the same graph. Segmented Eulerian Neighborhood Trails (SENT), a recent linearization method, converts graphs into sequences that can be perfectly decoded and efficiently processed by language models, but admit multi… ▽ More Autoregressive graph generators define likelihoods via a sequential construction process, but these likelihoods are only meaningful if they are consistent across all linearizations of the same graph. Segmented Eulerian Neighborhood Trails (SENT), a recent linearization method, converts graphs into sequences that can be perfectly decoded and efficiently processed by language models, but admit multiple equivalent linearizations of the same graph. We quantify violations in assigned negative log-likelihood (NLL) using the coefficient of variation across equivalent linearizations, which we call Linearization Uncertainty (LU). Training transformers under four linearization strategies on two datasets, we show that biased orderings achieve lower NLL on their native order but exhibit expected calibration error (ECE) two orders of magnitude higher under random permutation, indicating that these models have learned their training linearization rather than the underlying graph. On the molecular graph benchmark QM9, NLL for generated graphs is negatively correlated with molecular stability (AUC $=0.43$), while LU achieves AUC $=0.85$, suggesting that permutation-based evaluation provides a more reliable quality check for generated molecules. Code is available at https://github.com/lauritsf/linearization-uncertainty △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: Workshop 'Towards Trustworthy Predictions: Theory and Applications of Calibration for Modern AI' at AISTATS 2026, Tangier, Morocco

Practical Deep Heteroskedastic Regression

Authors: Mikkel Jordahn, Jonas Vestergaard Jensen, James Harrison, Michael Riis Andersen, Mikkel N. Schmidt

Abstract: Uncertainty quantification (UQ) in deep learning regression is of wide interest, as it supports critical applications including sequential decision making and risk-sensitive tasks. In heteroskedastic regression, where the uncertainty of the target depends on the input, a common approach is to train a neural network that parameterizes the mean and the variance of the predictive distribution. Still,… ▽ More Uncertainty quantification (UQ) in deep learning regression is of wide interest, as it supports critical applications including sequential decision making and risk-sensitive tasks. In heteroskedastic regression, where the uncertainty of the target depends on the input, a common approach is to train a neural network that parameterizes the mean and the variance of the predictive distribution. Still, training deep heteroskedastic regression models poses practical challenges in the trade-off between uncertainty quantification and mean prediction, such as optimization difficulties, representation collapse, and variance overfitting. In this work we identify previously undiscussed fallacies and propose a simple and efficient procedure that addresses these challenges jointly by post-hoc fitting a variance model across the intermediate layers of a pretrained network on a hold-out dataset. We demonstrate that our method achieves on-par or state-of-the-art uncertainty quantification on several molecular graph datasets, without compromising mean prediction accuracy and remaining cheap to use at prediction time. △ Less

Submitted 2 March, 2026; originally announced March 2026.

Cloud parameter estimation for interacting BEC after time-of-flight

Authors: Rasmus Malthe Fiil Andersen, Stine Frederiksen, Laurits Stokholm, Ilja Zebergs, Mick Kristensen, Carrie Weidner, Jan Joachim Arlt

Abstract: Experiments on Bose-Einstein condensates at finite temperature typically extract the system parameters, such as temperature, atom number, and condensed fraction from time-of-flight images taken after a free expansion time. This paper systematically examines the effect of repulsive interactions between the condensed and thermal atoms in partially condensed clouds on the expansion profile of the the… ▽ More Experiments on Bose-Einstein condensates at finite temperature typically extract the system parameters, such as temperature, atom number, and condensed fraction from time-of-flight images taken after a free expansion time. This paper systematically examines the effect of repulsive interactions between the condensed and thermal atoms in partially condensed clouds on the expansion profile of the thermal cloud. An analytical expression for the expansion can be obtained only if the interactions between the Bose-Einstein condensate and thermal atoms are neglected, resulting in a Bose-enhanced distribution for the thermal component. Here, the deformation of the cloud due to interactions and the effects on estimated parameters are investigated by simulating the expansion using a ballistic approximation. By fitting the simulated expansion profiles with a Bose-enhanced distribution, the errors of using such a fit are estimated, and the results are explained phenomenologically. The simulation was also used as a fitting function for experimental data, showing better agreement of the extracted condensed fraction with the semi-ideal model than results from a Bose-enhanced fit. △ Less

Submitted 15 January, 2026; originally announced January 2026.

Journal ref: J. Phys. B: At. Mol. Opt. Phys. 59 095502 (2026)

Duration-modulated neural population dynamics in humans during BMI controls

Authors: Fei Yin, Charles Guan, Tyson Aflalo, Jorge Gamez, Kelsie Pejsa, Emily Rosario, Charles Liu, Ausaf Bari, Richard Andersen

Abstract: The motor cortex (MC) is often described as an autonomous dynamical system during movement execution. In an autonomous dynamical system, flexible movement generation depends on reconfiguring the initial conditions, which then unwind along known dynamics. An open question is whether these dynamics govern MC activity during brain-machine interface (BMI) control. We investigated MC activity during BM… ▽ More The motor cortex (MC) is often described as an autonomous dynamical system during movement execution. In an autonomous dynamical system, flexible movement generation depends on reconfiguring the initial conditions, which then unwind along known dynamics. An open question is whether these dynamics govern MC activity during brain-machine interface (BMI) control. We investigated MC activity during BMI cursor movements of multiple durations, ranging from hundreds of milliseconds to sustained over seconds. These durations were chosen to cover the range of movement durations necessary to control modern BMIs under varying precision levels. Movements shared their MC initial condition with movements of different durations in the same direction. Long-duration movements sustained MC activity, effectively pausing the neural population dynamics until each movement goal was reached. The difference across durations in MC population dynamics may be attributed to external inputs. Our results highlight the role of sustained inputs to MC during movement. △ Less

Submitted 6 November, 2025; originally announced November 2025.

Control of a commercially available vehicle by a tetraplegic human using a brain-computer interface

Authors: Xinyun Zou, Jorge Gamez, Meghna Menon, Phillip Ring, Chadwick Boulay, Likhith Chitneni, Jackson Brennecke, Shana R. Melby, Gracy Kureel, Kelsie Pejsa, Emily R. Rosario, Ausaf A. Bari, Aniruddh Ravindran, Tyson Aflalo, Spencer S. Kellis, Dimitar Filev, Florian Solzbacher, Richard A. Andersen

Abstract: Brain-computer interfaces (BCIs) read neural signals directly from the brain to infer motor planning and execution. However, the implementation of this technology has been largely limited to laboratory settings, with few real-world applications. We developed a BCI system to drive a vehicle in both simulated and real-world environments. We demonstrate that an individual with tetraplegia, implanted… ▽ More Brain-computer interfaces (BCIs) read neural signals directly from the brain to infer motor planning and execution. However, the implementation of this technology has been largely limited to laboratory settings, with few real-world applications. We developed a BCI system to drive a vehicle in both simulated and real-world environments. We demonstrate that an individual with tetraplegia, implanted with intracortical BCI electrodes in the posterior parietal cortex (PPC) and the hand knob region of the motor cortex (MC), reacts at least as fast and precisely as motor intact participants. This BCI participant, living in California, could also remotely drive a Ford Mustang Mach-E vehicle in Michigan. Our teledriving tasks relied on cursor movement control for speed and steering in a closed urban test facility and through a predefined obstacle course. These two tasks serve as a proof-of-concept that takes into account the safety and feasibility of BCI-controlled driving. The final BCI system added click control for full-stop braking and thus enabled bimanual cursor-and-click control for simulated town driving with the same proficiency level as the motor intact control group through a virtual town with traffic. This first-of-its-kind implantable BCI application not only highlights the versatility and innovative potentials of BCIs but also illuminates the promising future for the development of life-changing solutions to improve independent mobility for those who suffer catastrophic neurological injury. △ Less

Submitted 26 March, 2026; v1 submitted 15 August, 2025; originally announced August 2025.

Comments: 50 pages, 7 figures, 1 table. 27 supplementary pages, 9 supplementary figures, 13 supplementary tables, 9 supplementary movies available as ancillary files

Distinct Lifetimes for $X$ and $Z$ Loop Measurements in a Majorana Tetron Device

Authors: Morteza Aghaee, Zulfi Alam, Rikke Andersen, Mariusz Andrzejczuk, Andrey Antipov, Mikhail Astafev, Lukas Avilovas, Ahmad Azizimanesh, Eric Banek, Bela Bauer, Jonathan Becker, Umesh Kumar Bhaskar, Andrea G. Boa, Srini Boddapati, Nichlaus Bohac, Jouri D. S. Bommer, Jan Borovsky, Léo Bourdet, Samuel Boutin, Lucas Casparis, Srivatsa Chakravarthi, Hamidreza Chalabi, Benjamin J. Chapman, Nikolaos Chatzaras, Tzu-Chiao Chien , et al. (142 additional authors not shown)

Abstract: We present a hardware realization and measurements of a tetron qubit device in a superconductor-semiconductor heterostructure. The device architecture contains two parallel superconducting nanowires, which support four Majorana zero modes (MZMs) when tuned into the topological phase, and a trivial superconducting backbone. Two distinct readout interferometers are formed by connecting the supercond… ▽ More We present a hardware realization and measurements of a tetron qubit device in a superconductor-semiconductor heterostructure. The device architecture contains two parallel superconducting nanowires, which support four Majorana zero modes (MZMs) when tuned into the topological phase, and a trivial superconducting backbone. Two distinct readout interferometers are formed by connecting the superconducting structure to a series of quantum dots. We perform single-shot interferometric measurements of the fermion parity for the two loops, designed to implement Pauli-$X$ and $Z$ measurements of the tetron. Performing repeated single-shot measurements yields two widely separated time scales $τ_X = 14.5\pm 0.3 \, \mathrm{μs}$ and $τ_Z = 12.4\pm 0.4\, \mathrm{ms}$ for parity switches observed in the $X$ and $Z$ measurement loops, which we attribute to intra-wire parity switches and external quasiparticle poisoning, respectively. We estimate assignment errors of $\mathrm{err}^X_a=16\%$ and $\mathrm{err}^Z_a=0.5\%$ for $X$ and $Z$ measurement-based operations, respectively. △ Less

Submitted 4 September, 2025; v1 submitted 11 July, 2025; originally announced July 2025.

Comments: Extended discussion of switching dynamics and multiple time scales in App. A. Added App. C on alternative scenarios. Corrected Figs. 2(i) & A4. Explained the method for extracting multiple time scales from long time traces in App. A

Stable perturbative predictions for isolated photon production with a jet pair at large $m_{j_1 j_2}$

Authors: Jeppe R. Andersen, Andreas Maier, Malina Rosca, Giacomo Ruisi

Abstract: We present the first calculation of the high-energy corrections to the process of isolated photon production in association with two jets. These corrections both stabilise the perturbative behaviour in $m_{j_1j_2}$ and lead to a significantly improved description of data from a recent ATLAS measurement. We present the first calculation of the high-energy corrections to the process of isolated photon production in association with two jets. These corrections both stabilise the perturbative behaviour in $m_{j_1j_2}$ and lead to a significantly improved description of data from a recent ATLAS measurement. △ Less

Submitted 24 October, 2025; v1 submitted 20 June, 2025; originally announced June 2025.

Comments: 24 pages, 13 figures. v2: one reference added

Report number: CERN-TH-2025-120, DESY-25-109, IPPP/25/35

Journal ref: JHEP10(2025)196

Early Assessment of Artificial Lower Extremity Sensory Response Times and Proprioceptive Acuity via Sensory Cortex Electrical Stimulation

Authors: Won Joon Sohn, Jeffrey Lim, Po T. Wang, Susan J. Shaw, Michelle Armacost, Hui Gong, Brian Lee, Darrin Lee, Payam Heydari, Richard A. Andersen, Charles Y. Liu, Zoran Nenadic, An H. Do

Abstract: Bi-directional brain computer interfaces (BD-BCIs) may restore brain-controlled walking and artificial leg sensation after spinal cord injury. Current BD-BCIs provide only simplistic "tingling" feedback, which lacks proprioceptive information to perceive critical gait events (leg swing, double support). This information must also be perceived adequately fast to facilitate timely motor responses. H… ▽ More Bi-directional brain computer interfaces (BD-BCIs) may restore brain-controlled walking and artificial leg sensation after spinal cord injury. Current BD-BCIs provide only simplistic "tingling" feedback, which lacks proprioceptive information to perceive critical gait events (leg swing, double support). This information must also be perceived adequately fast to facilitate timely motor responses. Here, we investigated utilizing primary sensory cortex (S1) direct cortical electrical stimulation (DCES) to deliver leg proprioceptive information and measured response times to artificial leg sensations. Subjects with subdural electrocorticogram electrodes over S1 leg areas participated in two tasks: (1) Proprioceptive acuity: subjects identified the difference between DCES-induced percepts emulating various leg swing speeds; (2) Sensory response: measuring subjects' reaction time to DCES-induced leg sensations, with DCES-hand, visual and auditory control conditions. Three subjects were recruited. Only one completed the proprioceptive assessment, achieving 80%, 70%, 60%, and 53% accuracy in discriminating between fast/slow, fast/medium, medium/slow, and same speeds, respectively (p-value=1.9x10$^{-5}$). Response times for leg/hand percepts were 1007$\pm$413/599$\pm$171 ms, visual leg/hand responses were 528$\pm$137/384$\pm$84 ms, and auditory leg/hand responses were 393$\pm$106/352$\pm$93 ms, respectively. These results suggest proprioceptive information can be delivered artificially, but perception may be significantly delayed. Future work should address improving acuity, reducing response times, and expanding sensory modalities. △ Less

Submitted 28 May, 2025; originally announced May 2025.

Comments: 6 page conference proceeding pre-print, 3 figures, 4 tables

Real-Time Brain-Computer Interface Control of Walking Exoskeleton with Bilateral Sensory Feedback

Authors: Jeffrey Lim, Po T. Wang, Won Joon Sohn, Derrick Lin, Shravan Thaploo, Luke Bashford, David Bjanes, Angelica Nguyen, Hui Gong, Michelle Armacost, Susan J. Shaw, Spencer Kellis, Brian Lee, Darrin Lee, Payam Heydari, Richard A. Andersen, Zoran Nenadic, Charles Y. Liu, An H. Do

Abstract: Invasive brain-computer interface (BCI) technology has demonstrated the possibility of restoring brain-controlled walking in paraplegic spinal cord injury patients. However, current implementations of BCI-controlled walking still have significant drawbacks. In particular, prior systems are unidirectional and lack sensory feedback for insensate patients, have suboptimal reliance on brain signals fr… ▽ More Invasive brain-computer interface (BCI) technology has demonstrated the possibility of restoring brain-controlled walking in paraplegic spinal cord injury patients. However, current implementations of BCI-controlled walking still have significant drawbacks. In particular, prior systems are unidirectional and lack sensory feedback for insensate patients, have suboptimal reliance on brain signals from the bilateral arm areas of the motor cortex, and depend on external systems for signal processing. Motivated by these shortcomings, this study is the first time a bidirectional brain-computer interface (BDBCI) has demonstrated the restoration of both brain-controlled walking and leg sensory feedback while utilizing the bilateral leg motor and sensory cortices. Here, a subject undergoing subdural electrocorticogram electrode implantation for epilepsy surgery evaluation leveraged the leg representation areas of the bilateral interhemispheric primary motor and sensory cortices to operate a BDBCI with high performance. Although electrode implantation in the interhemispheric region is uncommon, electrodes can be safely implanted in this region to access rich leg motor information and deliver bilateral leg sensory feedback. Finally, we demonstrated that all BDBCI operations can be executed on a dedicated, portable embedded system. These results indicate that BDBCIs can potentially provide brain-controlled ambulation and artificial leg sensation to people with paraplegia after spinal cord injury in a manner that emulates full-implantability and is untethered from any external systems. △ Less

Submitted 30 April, 2025; originally announced May 2025.

Comments: Main text of pre-print and supplementary information included

On Local Posterior Structure in Deep Ensembles

Authors: Mikkel Jordahn, Jonas Vestergaard Jensen, Mikkel N. Schmidt, Michael Riis Andersen

Abstract: Bayesian Neural Networks (BNNs) often improve model calibration and predictive uncertainty quantification compared to point estimators such as maximum-a-posteriori (MAP). Similarly, deep ensembles (DEs) are also known to improve calibration, and therefore, it is natural to hypothesize that deep ensembles of BNNs (DE-BNNs) should provide even further improvements. In this work, we systematically in… ▽ More Bayesian Neural Networks (BNNs) often improve model calibration and predictive uncertainty quantification compared to point estimators such as maximum-a-posteriori (MAP). Similarly, deep ensembles (DEs) are also known to improve calibration, and therefore, it is natural to hypothesize that deep ensembles of BNNs (DE-BNNs) should provide even further improvements. In this work, we systematically investigate this across a number of datasets, neural network architectures, and BNN approximation methods and surprisingly find that when the ensembles grow large enough, DEs consistently outperform DE-BNNs on in-distribution data. To shine light on this observation, we conduct several sensitivity and ablation studies. Moreover, we show that even though DE-BNNs outperform DEs on out-of-distribution metrics, this comes at the cost of decreased in-distribution performance. As a final contribution, we open-source the large pool of trained models to facilitate further research on this topic. △ Less

Submitted 17 March, 2025; originally announced March 2025.

Comments: Code and models available at https://github.com/jonasvj/OnLocalPosteriorStructureInDeepEnsembles

GeoFormer: A Multi-Polygon Segmentation Transformer

Authors: Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen

Abstract: In remote sensing there exists a common need for learning scale invariant shapes of objects like buildings. Prior works relies on tweaking multiple loss functions to convert segmentation maps into the final scale invariant representation, necessitating arduous design and optimization. For this purpose we introduce the GeoFormer, a novel architecture which presents a remedy to the said challenges,… ▽ More In remote sensing there exists a common need for learning scale invariant shapes of objects like buildings. Prior works relies on tweaking multiple loss functions to convert segmentation maps into the final scale invariant representation, necessitating arduous design and optimization. For this purpose we introduce the GeoFormer, a novel architecture which presents a remedy to the said challenges, learning to generate multipolygons end-to-end. By modeling keypoints as spatially dependent tokens in an auto-regressive manner, the GeoFormer outperforms existing works in delineating building objects from satellite imagery. We evaluate the robustness of the GeoFormer against former methods through a variety of parameter ablations and highlight the advantages of optimizing a single likelihood function. Our study presents the first successful application of auto-regressive transformer models for multi-polygon predictions in remote sensing, suggesting a promising methodological alternative for building vectorization. △ Less

Submitted 25 November, 2024; originally announced November 2024.

Comments: 21 pages, 5 figures, in proceedings of British Machine Vision Conference 2024

A Cell Resampler study of Negative Weights in Multi-jet Merged Samples

Authors: Jeppe R. Andersen, Ana Cueto, Stephen P. Jones, Andreas Maier

Abstract: We study the use of cell resampling to reduce the fraction of negatively weighted Monte Carlo events in a generated sample typical of that used in experimental analyses. To this end, we apply the Cell Resampler to a set of $pp \rightarrow γγ+ \mathrm{jets}$ shower-merged NLO matched events, describing the diphoton background to Higgs boson production, generated using the FxFx and MEPS@NLO merging… ▽ More We study the use of cell resampling to reduce the fraction of negatively weighted Monte Carlo events in a generated sample typical of that used in experimental analyses. To this end, we apply the Cell Resampler to a set of $pp \rightarrow γγ+ \mathrm{jets}$ shower-merged NLO matched events, describing the diphoton background to Higgs boson production, generated using the FxFx and MEPS@NLO merging procedures and showered using the Pythia and Sherpa parton shower algorithms. We discuss the impact on various kinematic distributions. △ Less

Submitted 18 November, 2024; originally announced November 2024.

Report number: CERN-TH-2024-200, IPPP/24/73

EB-NeRD: A Large-Scale Dataset for News Recommendation

Authors: Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen

Abstract: Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million un… ▽ More Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million unique users and more than 37 million impression logs from Ekstra Bladet. It also includes a collection of over 125,000 Danish news articles, complete with titles, abstracts, bodies, and metadata, such as categories. EB-NeRD served as the benchmark dataset for the RecSys '24 Challenge, where it was demonstrated how the dataset can be used to address both technical and normative challenges in designing effective and responsible recommender systems for news publishing. The dataset is available at: https://recsys.eb.dk. △ Less

Submitted 4 October, 2024; originally announced October 2024.

Comments: 11 pages, 8 tables, 2 figures, RecSys '24

RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations

Authors: Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen

Abstract: The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. This paper describes the challenge, including its objectives, problem setting, and the dataset provided by the Danish news publishers Ekstra Bladet and JP/Politikens Media Group ("Ekstra Blad… ▽ More The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. This paper describes the challenge, including its objectives, problem setting, and the dataset provided by the Danish news publishers Ekstra Bladet and JP/Politikens Media Group ("Ekstra Bladet"). The challenge explores the unique aspects of news recommendation, such as modeling user preferences based on behavior, accounting for the influence of the news agenda on user interests, and managing the rapid decay of news items. Additionally, the challenge embraces normative complexities, investigating the effects of recommender systems on news flow and their alignment with editorial values. We summarize the challenge setup, dataset characteristics, and evaluation metrics. Finally, we announce the winners and highlight their contributions. The dataset is available at: https://recsys.eb.dk. △ Less

Submitted 30 September, 2024; originally announced September 2024.

Comments: 5 pages, 3 tables, RecSys' 24

Variance reduction of diffusion model's gradients with Taylor approximation-based control variate

Authors: Paul Jeha, Will Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen

Abstract: Score-based models, trained with denoising score matching, are remarkably effective in generating high dimensional data. However, the high variance of their training objective hinders optimisation. We attempt to reduce it with a control variate, derived via a $k$-th order Taylor expansion on the training objective and its gradient. We prove an equivalence between the two and demonstrate empiricall… ▽ More Score-based models, trained with denoising score matching, are remarkably effective in generating high dimensional data. However, the high variance of their training objective hinders optimisation. We attempt to reduce it with a control variate, derived via a $k$-th order Taylor expansion on the training objective and its gradient. We prove an equivalence between the two and demonstrate empirically the effectiveness of our approach on a low dimensional problem setting; and study its effect on larger problems. △ Less

Submitted 22 August, 2024; originally announced August 2024.

Comments: 14 pages, ICML Structured Probabilistic Inference & Generative Modeling 2024

A Partial Near-infrared Guide Star Catalog for Thirty Meter Telescope Operations

Authors: Sarang Shah, Smitha Subramanian, Avinash C. K., David R. Andersen, Warren Skidmore, G. C. Anupama, Francisco Delgado, Kim Gillies, Maheshwar Gopinathan, A. N. Ramaprakash, B. E. Reddy, T. Sivarani, Annapurni Subramaniam

Abstract: At first light, the Thirty Meter Telescope (TMT) near-infrared (NIR) instruments will be fed by a multiconjugate adaptive optics instrument known as the Narrow Field Infrared Adaptive Optics System (NFIRAOS). NFIRAOS will use six laser guide stars to sense atmospheric turbulence in a volume corresponding to a field of view of 2', but natural guide stars (NGSs) will be required to sense tip/tilt an… ▽ More At first light, the Thirty Meter Telescope (TMT) near-infrared (NIR) instruments will be fed by a multiconjugate adaptive optics instrument known as the Narrow Field Infrared Adaptive Optics System (NFIRAOS). NFIRAOS will use six laser guide stars to sense atmospheric turbulence in a volume corresponding to a field of view of 2', but natural guide stars (NGSs) will be required to sense tip/tilt and focus. To achieve high sky coverage (50% at the north Galactic pole), the NFIRAOS client instruments use NIR on-instrument wavefront sensors that take advantage of the sharpening of the stars by NFIRAOS. A catalog of guide stars with NIR magnitudes as faint as 22 mag in the J band (Vega system), covering the TMT-observable sky, will be a critical resource for the efficient operation of NFIRAOS, and no such catalog currently exists. Hence, it is essential to develop such a catalog by computing the expected NIR magnitudes of stellar sources identified in deep optical sky surveys using their optical magnitudes. This paper discusses the generation of a partial NIR Guide Star Catalog (IRGSC), similar to the final IRGSC for TMT operations. The partial catalog is generated by applying stellar atmospheric models to the optical data of stellar sources from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) optical data and then computing their expected NIR magnitudes. We validated the computed NIR magnitudes of the sources in some fields by using the available NIR data for those fields. We identified the remaining challenges of this approach. We outlined the path for producing the final IRGSC using the Pan-STARRS data. We have named the Python code to generate the IRGSC as irgsctool, which generates a list of NGS for a field using optical data from the Pan-STARRS 3pi survey and also a list of NGSs having observed NIR data from the UKIRT Infrared Deep Sky Survey if they are available. △ Less

Submitted 15 August, 2024; originally announced August 2024.

Journal ref: The Astronomical Journal, 168:59 (28pp), 2024 August

The Use of Generative Search Engines for Knowledge Work and Complex Tasks

Authors: Siddharth Suri, Scott Counts, Leijie Wang, Chacha Chen, Mengting Wan, Tara Safavi, Jennifer Neville, Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Sathish Manivannan, Nagu Rangan, Longqi Yang

Abstract: Until recently, search engines were the predominant method for people to access online information. The recent emergence of large language models (LLMs) has given machines new capabilities such as the ability to generate new digital artifacts like text, images, code etc., resulting in a new tool, a generative search engine, which combines the capabilities of LLMs with a traditional search engine.… ▽ More Until recently, search engines were the predominant method for people to access online information. The recent emergence of large language models (LLMs) has given machines new capabilities such as the ability to generate new digital artifacts like text, images, code etc., resulting in a new tool, a generative search engine, which combines the capabilities of LLMs with a traditional search engine. Through the empirical analysis of Bing Copilot (Bing Chat), one of the first publicly available generative search engines, we analyze the types and complexity of tasks that people use Bing Copilot for compared to Bing Search. Findings indicate that people use the generative search engine for more knowledge work tasks that are higher in cognitive complexity than were commonly done with a traditional search engine. △ Less

Submitted 19 March, 2024; originally announced April 2024.

Comments: 32 pages, 3 figures, 4 tables

ACM Class: J.4

Atom Number Fluctuations in Bose Gases -- Statistical analysis of parameter estimation

Authors: Toke Vibel, Mikkel Berg Christensen, Rasmus Malthe Fiil Andersen, Laurits Nikolaj Stokholm, Krzysztof Pawłowski, Kazimierz Rzążewski, Mick Althoff Kristensen, Jan Joachim Arlt

Abstract: The investigation of the fluctuations in interacting quantum systems at finite temperatures showcases the ongoing challenges in understanding complex quantum systems. Recently, atom number fluctuations in weakly interacting Bose-Einstein condensates were observed, motivating an investigation of the thermal component of partially condensed Bose gases. Here, we present a combined analysis of both co… ▽ More The investigation of the fluctuations in interacting quantum systems at finite temperatures showcases the ongoing challenges in understanding complex quantum systems. Recently, atom number fluctuations in weakly interacting Bose-Einstein condensates were observed, motivating an investigation of the thermal component of partially condensed Bose gases. Here, we present a combined analysis of both components, revealing the presence of fluctuations in the thermal component. This analysis includes a comprehensive statistical evaluation of uncertainties in the preparation and parameter estimation of partially condensed Bose gases. Using Monte Carlo simulations of optical density profiles, we estimate the noise contributions to the atom number and temperature estimation of the condensed and thermal cloud, which is generally applicable in the field of ultracold atoms. Furthermore, we investigate the specific noise contributions in the analysis of atom number fluctuations and show that preparation noise in the total atom number leads to an important technical noise contribution. Subtracting all known noise contributions from the variance of the atom number in the BEC and thermal component allows us to improve the estimate of the fundamental peak fluctuations. △ Less

Submitted 22 March, 2024; originally announced March 2024.

Journal ref: J. Phys. B: At. Mol. Opt. Phys. 57 195301 (2024)

Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models

Authors: Ying-Chun Lin, Jennifer Neville, Jack W. Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, Jaime Teevan

Abstract: Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational systems. Existing approaches based on featur… ▽ More Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational systems. Existing approaches based on featurized ML models or text embeddings fall short in extracting generalizable patterns and are hard to interpret. In this work, we show that LLMs can extract interpretable signals of user satisfaction from their natural language utterances more effectively than embedding-based approaches. Moreover, an LLM can be tailored for USE via an iterative prompting framework using supervision from labeled examples. The resulting method, Supervised Prompting for User satisfaction Rubrics (SPUR), not only has higher accuracy but is more interpretable as it scores user satisfaction via learned rubrics with a detailed breakdown. △ Less

Submitted 8 June, 2024; v1 submitted 18 March, 2024; originally announced March 2024.

TnT-LLM: Text Mining at Scale with Large Language Models

Authors: Mengting Wan, Tara Safavi, Sujay Kumar Jauhar, Yujin Kim, Scott Counts, Jennifer Neville, Siddharth Suri, Chirag Shah, Ryen W White, Longqi Yang, Reid Andersen, Georg Buscher, Dhruv Joshi, Nagu Rangan

Abstract: Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and building text-based label classifiers still rely heavily on domain expertise and manual curation, making the process expensive and time-consuming. Thi… ▽ More Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application. However, most existing methods for producing label taxonomies and building text-based label classifiers still rely heavily on domain expertise and manual curation, making the process expensive and time-consuming. This is particularly challenging when the label space is under-specified and large-scale data annotations are unavailable. In this paper, we address these challenges with Large Language Models (LLMs), whose prompt-based interface facilitates the induction and use of large-scale pseudo labels. We propose TnT-LLM, a two-phase framework that employs LLMs to automate the process of end-to-end label generation and assignment with minimal human effort for any given use-case. In the first phase, we introduce a zero-shot, multi-stage reasoning approach which enables LLMs to produce and refine a label taxonomy iteratively. In the second phase, LLMs are used as data labelers that yield training samples so that lightweight supervised classifiers can be reliably built, deployed, and served at scale. We apply TnT-LLM to the analysis of user intent and conversational domain for Bing Copilot (formerly Bing Chat), an open-domain chat-based search engine. Extensive experiments using both human and automatic evaluation metrics demonstrate that TnT-LLM generates more accurate and relevant label taxonomies when compared against state-of-the-art baselines, and achieves a favorable balance between accuracy and efficiency for classification at scale. We also share our practical experiences and insights on the challenges and opportunities of using LLMs for large-scale text mining in real-world applications. △ Less

Submitted 18 March, 2024; originally announced March 2024.

Comments: 9 pages main content, 8 pages references and appendix

Early feasibility of an embedded bi-directional brain-computer interface for ambulation

Authors: Jeffrey Lim, Po T. Wang, Wonjoon Sohn, Claudia Serrano-Amenos, Mina Ibrahim, Derrick Lin, Shravan Thaploo, Susan J. Shaw, Michelle Armacost, Hui Gong, Brian Lee, Darrin Lee, Richard A. Andersen, Payam Heydari, Charles Y. Liu, Zoran Nenadic, An H. Do

Abstract: Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have shown promise in restoring or replacing lowe… ▽ More Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have shown promise in restoring or replacing lower extremity motor functions, none so far have simultaneously implemented sensory feedback functions. Additionally, many existing BCIs for ambulation rely on bulky external hardware that make them ill-suited for non-research settings. Here, we present an embedded bi-directional BCI (BDBCI), that restores motor function by enabling neural control over a robotic gait exoskeleton (RGE) and delivers sensory feedback via direct cortical electrical stimulation (DCES) in response to RGE leg swing. A first demonstration with this system was performed with a single subject implanted with electrocorticography electrodes, achieving an average lag-optimized cross-correlation of 0.80$\pm$0.08 between cues and decoded states over 5 runs. △ Less

Submitted 18 February, 2024; originally announced February 2024.

Comments: 5 pages, 6 figures, two tables, also submitted to IEEE EMBC 2024 conference

MSC Class: 92C55

Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein Instances

Authors: Ronja Güldenring, Rasmus Eckholdt Andersen, Lazaros Nalpantidis

Abstract: Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones. When humans look at plants, they naturally perceive the plant architecture with its individual leaves and branching system. In this work, we want to advance the granularity in plant understanding for agri… ▽ More Robot perception is far from what humans are capable of. Humans do not only have a complex semantic scene understanding but also extract fine-grained intra-object properties for the salient ones. When humans look at plants, they naturally perceive the plant architecture with its individual leaves and branching system. In this work, we want to advance the granularity in plant understanding for agricultural precision robots. We develop a model to extract fine-grained phenotypic information, such as leaf-, stem-, and vein instances. The underlying dataset RumexLeaves is made publicly available and is the first of its kind with keypoint-guided polyline annotations leading along the line from the lowest stem point along the leaf basal to the leaf apex. Furthermore, we introduce an adapted metric POKS complying with the concept of keypoint-guided polylines. In our experimental evaluation, we provide baseline results for our newly introduced dataset while showcasing the benefits of POKS over OKS. △ Less

Submitted 14 December, 2023; originally announced December 2023.

Comments: Accepted at Robotics and Automation Letters (RA-L)

Neural machine translation for automated feedback on children's early-stage writing

Authors: Jonas Vestergaard Jensen, Mikkel Jordahn, Michael Riis Andersen

Abstract: In this work, we address the problem of assessing and constructing feedback for early-stage writing automatically using machine learning. Early-stage writing is typically vastly different from conventional writing due to phonetic spelling and lack of proper grammar, punctuation, spacing etc. Consequently, early-stage writing is highly non-trivial to analyze using common linguistic metrics. We prop… ▽ More In this work, we address the problem of assessing and constructing feedback for early-stage writing automatically using machine learning. Early-stage writing is typically vastly different from conventional writing due to phonetic spelling and lack of proper grammar, punctuation, spacing etc. Consequently, early-stage writing is highly non-trivial to analyze using common linguistic metrics. We propose to use sequence-to-sequence models for "translating" early-stage writing by students into "conventional" writing, which allows the translated text to be analyzed using linguistic metrics. Furthermore, we propose a novel robust likelihood to mitigate the effect of noise in the dataset. We investigate the proposed methods using a set of numerical experiments and demonstrate that the conventional text can be predicted with high accuracy. △ Less

Submitted 15 November, 2023; originally announced November 2023.

Comments: 9 pages, 1 figure, 1 table, to be published in the proceedings of the Northern Lights Deep Learning Conference 2024

ACM Class: I.2.7

Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies

Authors: Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Scott Counts, Sarkar Snigdha Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, Nagu Rangan, Tara Safavi, Siddharth Suri, Mengting Wan, Leijie Wang, Longqi Yang

Abstract: Log data can reveal valuable information about how users interact with Web search services, what they want, and how satisfied they are. However, analyzing user intents in log data is not easy, especially for emerging forms of Web search such as AI-driven chat. To understand user intents from log data, we need a way to label them with meaningful categories that capture their diversity and dynamics.… ▽ More Log data can reveal valuable information about how users interact with Web search services, what they want, and how satisfied they are. However, analyzing user intents in log data is not easy, especially for emerging forms of Web search such as AI-driven chat. To understand user intents from log data, we need a way to label them with meaningful categories that capture their diversity and dynamics. Existing methods rely on manual or machine-learned labeling, which are either expensive or inflexible for large and dynamic datasets. We propose a novel solution using large language models (LLMs), which can generate rich and relevant concepts, descriptions, and examples for user intents. However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop. To address this, we propose a new methodology with human experts and assessors to verify the quality of the LLM-generated taxonomy. We also present an end-to-end pipeline that uses an LLM with human-in-the-loop to produce, refine, and apply labels for user intent analysis in log data. We demonstrate its effectiveness by uncovering new insights into user intents from search and chat logs from the Microsoft Bing commercial search engine. The proposed work's novelty stems from the method for generating purpose-driven user intent taxonomies with strong validation. This method not only helps remove methodological and practical bottlenecks from intent-focused research, but also provides a new framework for generating, validating, and applying other kinds of taxonomies in a scalable and adaptable way with reasonable human effort. △ Less

Submitted 9 May, 2024; v1 submitted 14 September, 2023; originally announced September 2023.

Report number: MSR-TR-2023-32

S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs

Authors: Sarkar Snigdha Sarathi Das, Chirag Shah, Mengting Wan, Jennifer Neville, Longqi Yang, Reid Andersen, Georg Buscher, Tara Safavi

Abstract: The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations. While sufficient for task-oriented dialogue systems supporting narrow domain applications, the advent of Large Language Model (LLM)-based chat systems has introduced many real-world intricacies in open-domain dialogues. These intricacies manifest in the form of increased co… ▽ More The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations. While sufficient for task-oriented dialogue systems supporting narrow domain applications, the advent of Large Language Model (LLM)-based chat systems has introduced many real-world intricacies in open-domain dialogues. These intricacies manifest in the form of increased complexity in contextual interactions, extended dialogue sessions encompassing a diverse array of topics, and more frequent contextual shifts. To handle these intricacies arising from evolving LLM-based chat systems, we propose joint dialogue segmentation and state tracking per segment in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a true open-domain dialogue system, we propose S3-DST, a structured prompting technique that harnesses Pre-Analytical Recollection, a novel grounding mechanism we designed for improving long context tracking. To demonstrate the efficacy of our proposed approach in joint segmentation and state tracking, we evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as well as publicly available DST and segmentation datasets. Across all datasets and settings, S3-DST consistently outperforms the state-of-the-art, demonstrating its potency and robustness the next generation of LLM-based chat systems. △ Less

Submitted 15 September, 2023; originally announced September 2023.

Linking Symptom Inventories using Semantic Textual Similarity

Authors: Eamonn Kennedy, Shashank Vadlamani, Hannah M Lindsey, Kelly S Peterson, Kristen Dams OConnor, Kenton Murray, Ronak Agarwal, Houshang H Amiri, Raeda K Andersen, Talin Babikian, David A Baron, Erin D Bigler, Karen Caeyenberghs, Lisa Delano-Wood, Seth G Disner, Ekaterina Dobryakova, Blessen C Eapen, Rachel M Edelstein, Carrie Esopenko, Helen M Genova, Elbert Geuze, Naomi J Goodrich-Hunsaker, Jordan Grafman, Asta K Haberg, Cooper B Hodges , et al. (57 additional authors not shown)

Abstract: An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores… ▽ More An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment. △ Less

Submitted 8 September, 2023; originally announced September 2023.

Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Authors: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac, R. Abbott, H. Abe, F. Acernese, K. Ackley, C. Adamcewicz, S. Adhicary, N. Adhikari, R. X. Adhikari, V. K. Adkins, V. B. Adya, C. Affeldt, D. Agarwal, M. Agathos, O. D. Aguiar, I. Aguilar, L. Aiello, A. Ain, P. Ajith, T. Akutsu, S. Albanesi, R. A. Alfaidi , et al. (1750 additional authors not shown)

Abstract: Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effect… ▽ More Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level. △ Less

Submitted 7 August, 2023; originally announced August 2023.

Comments: 24 pages, 5 figures

Report number: LIGO-P2300080

Exploring high-purity multi-parton scattering at hadron colliders

Authors: Jeppe R. Andersen, Pier Francesco Monni, Luca Rottoli, Gavin P. Salam, Alba Soto-Ontoso

Abstract: Multi-parton interactions are a fascinating phenomenon that occur in almost every high-energy hadron--hadron collision, yet are remarkably difficult to study quantitatively. In this letter we present a strategy to optimally disentangle multi-parton interactions from the primary scattering in a collision. That strategy enables probes of multi-parton interactions that are significantly beyond the st… ▽ More Multi-parton interactions are a fascinating phenomenon that occur in almost every high-energy hadron--hadron collision, yet are remarkably difficult to study quantitatively. In this letter we present a strategy to optimally disentangle multi-parton interactions from the primary scattering in a collision. That strategy enables probes of multi-parton interactions that are significantly beyond the state of the art, including their characteristic momentum scale, the interconnection between primary and secondary scatters, and the pattern of three and potentially even more simultaneous hard scatterings. This opens a path to powerful new constraints on multi-parton interactions for LHC phenomenology and to the investigation of their rich field-theoretical structure. △ Less

Submitted 17 May, 2024; v1 submitted 11 July, 2023; originally announced July 2023.

Comments: 5 pages + supplementary material. Matches version published in PRL

Report number: CERN-TH-2023-055, DCPT/23/54, IPPP/23/27, OUTP-23-04P, ZU-TH 17/23

Polygonizer: An auto-regressive building delineator

Authors: Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen

Abstract: In geospatial planning, it is often essential to represent objects in a vectorized format, as this format easily translates to downstream tasks such as web development, graphics, or design. While these problems are frequently addressed using semantic segmentation, which requires additional post-processing to vectorize objects in a non-trivial way, we present an Image-to-Sequence model that allows… ▽ More In geospatial planning, it is often essential to represent objects in a vectorized format, as this format easily translates to downstream tasks such as web development, graphics, or design. While these problems are frequently addressed using semantic segmentation, which requires additional post-processing to vectorize objects in a non-trivial way, we present an Image-to-Sequence model that allows for direct shape inference and is ready for vector-based workflows out of the box. We demonstrate the model's performance in various ways, including perturbations to the image input that correspond to variations or artifacts commonly encountered in remote sensing applications. Our model outperforms prior works when using ground truth bounding boxes (one object per image), achieving the lowest maximum tangent angle error. △ Less

Submitted 8 April, 2023; originally announced April 2023.

Comments: ICLR 2023 Workshop on Machine Learning in Remote Sensing

HEJ 2.2: W boson pairs and Higgs boson plus jet production at high energies

Authors: Jeppe R. Andersen, Bertrand Ducloué, Conor Elrick, Hitham Hassan, Andreas Maier, Graeme Nail, Jérémy Paltrinieri, Andreas Papaefstathiou, Jennifer M. Smillie

Abstract: We present version 2.2 of the High Energy Jets (HEJ) Monte Carlo event generator for hadronic scattering processes at high energies. The new version adds support for two further processes of central phenomenological interest, namely the production of a W boson pair with equal charge together with two or more jets and the production of a Higgs boson with at least one jet. Furthermore, a new predict… ▽ More We present version 2.2 of the High Energy Jets (HEJ) Monte Carlo event generator for hadronic scattering processes at high energies. The new version adds support for two further processes of central phenomenological interest, namely the production of a W boson pair with equal charge together with two or more jets and the production of a Higgs boson with at least one jet. Furthermore, a new prediction for charged lepton pair production with high jet multiplicities is provided in the high-energy limit. The accuracy of HEJ 2.2 can be increased further through an enhanced interface to standard predictions based on conventional perturbation theory. We describe all improvements and provide extensive usage examples. HEJ 2.2 can be obtained from https://hej.hepforge.org. △ Less

Submitted 19 January, 2024; v1 submitted 28 March, 2023; originally announced March 2023.

Comments: 36 pages, 6 figures and many code listings. Journal version

Report number: IPPP/23/18, DCPT/23/36, DESY-23-038

Journal ref: SciPost Phys. Codebases 21 (2023)

Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples

Authors: Jeppe R. Andersen, Andreas Maier, Daniel Maître

Abstract: We demonstrate that cell resampling can eliminate the bulk of negative event weights in large event samples of high multiplicity processes without discernible loss of accuracy in the predicted observables. The application of cell resampling to much larger data sets and higher multiplicity processes such as vector boson production with up to five jets has been made possible by improvements in the m… ▽ More We demonstrate that cell resampling can eliminate the bulk of negative event weights in large event samples of high multiplicity processes without discernible loss of accuracy in the predicted observables. The application of cell resampling to much larger data sets and higher multiplicity processes such as vector boson production with up to five jets has been made possible by improvements in the method paired with drastic enhancement of the computational efficiency of the implementation. △ Less

Submitted 25 September, 2023; v1 submitted 27 March, 2023; originally announced March 2023.

Comments: 15 pages, 4 figures. Journal version

Report number: IPPP/23/15, DCPT/23/30, DESY-23-037

Journal ref: Eur.Phys.J.C 83 (2023) 9, 835

Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery

Authors: Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez, Jes Frellsen, Michael Riis Andersen

Abstract: We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a set of regular patterns. Unlike alternative methods that require multiple images of the same object, our approach enables estimating 3D roof meshes using only a single image for predictions. The approach employs the PolyG… ▽ More We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a set of regular patterns. Unlike alternative methods that require multiple images of the same object, our approach enables estimating 3D roof meshes using only a single image for predictions. The approach employs the PolyGen, a deep generative transformer architecture for 3D meshes. We apply this model in a new domain and investigate the sensitivity of the image resolution. We propose a novel metric to evaluate the performance of the inferred meshes, and our results show that the model is robust even at lower resolutions, while qualitatively producing realistic representations for out-of-distribution samples. △ Less

Submitted 20 March, 2023; originally announced March 2023.

Comments: Copyright 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Open data from the third observing run of LIGO, Virgo, KAGRA and GEO

Authors: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, R. Abbott, H. Abe, F. Acernese, K. Ackley, S. Adhicary, N. Adhikari, R. X. Adhikari, V. K. Adkins, V. B. Adya, C. Affeldt, D. Agarwal, M. Agathos, O. D. Aguiar, L. Aiello, A. Ain, P. Ajith, T. Akutsu, S. Albanesi, R. A. Alfaidi, A. Al-Jodah, C. Alléné, A. Allocca , et al. (1719 additional authors not shown)

Abstract: The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasti… ▽ More The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages. △ Less

Submitted 7 February, 2023; originally announced February 2023.

Comments: 27 pages, 3 figures

Report number: LIGO-P2200316

On the role of Model Uncertainties in Bayesian Optimization

Authors: Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen, Michael Riis Andersen

Abstract: Bayesian optimization (BO) is a popular method for black-box optimization, which relies on uncertainty as part of its decision-making process when deciding which experiment to perform next. However, not much work has addressed the effect of uncertainty on the performance of the BO algorithm and to what extent calibrated uncertainties improve the ability to find the global optimum. In this work, we… ▽ More Bayesian optimization (BO) is a popular method for black-box optimization, which relies on uncertainty as part of its decision-making process when deciding which experiment to perform next. However, not much work has addressed the effect of uncertainty on the performance of the BO algorithm and to what extent calibrated uncertainties improve the ability to find the global optimum. In this work, we provide an extensive study of the relationship between the BO performance (regret) and uncertainty calibration for popular surrogate models and compare them across both synthetic and real-world experiments. Our results confirm that Gaussian Processes are strong surrogate models and that they tend to outperform other popular models. Our results further show a positive association between calibration error and regret, but interestingly, this association disappears when we control for the type of model in the analysis. We also studied the effect of re-calibration and demonstrate that it generally does not lead to improved regret. Finally, we provide theoretical justification for why uncertainty calibration might be difficult to combine with BO due to the small sample sizes commonly used. △ Less

Submitted 14 January, 2023; originally announced January 2023.

Comments: 14 pages, 4 figures, 2 tables

SolarDK: A high-resolution urban solar panel image classification and localization dataset

Authors: Maxim Khomiakov, Julius Holbech Radzikowski, Carl Anton Schmidt, Mathias Bonde Sørensen, Mads Andersen, Michael Riis Andersen, Jes Frellsen

Abstract: The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data fr… ▽ More The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/. △ Less

Submitted 2 December, 2022; originally announced December 2022.

Comments: 7 pages, 2 figures, to access the dataset, see https://osf.io/aj539/

High Energy Resummed Predictions for the Production of a Higgs Boson with at least One Jet

Authors: Jeppe R. Andersen, Hitham Hassan, Andreas Maier, Jérémy Paltrinieri, Andreas Papaefstathiou, Jennifer M. Smillie

Abstract: We present all-order predictions for Higgs boson production plus at least one jet which are accurate to leading logarithm in $\hat s/|p_\perp|^2$. Our calculation includes full top and bottom quark mass dependence at all orders in the logarithmic part, and to highest available order in the tree-level matching. The calculation is implemented in the framework of High Energy Jets (HEJ). This is the f… ▽ More We present all-order predictions for Higgs boson production plus at least one jet which are accurate to leading logarithm in $\hat s/|p_\perp|^2$. Our calculation includes full top and bottom quark mass dependence at all orders in the logarithmic part, and to highest available order in the tree-level matching. The calculation is implemented in the framework of High Energy Jets (HEJ). This is the first cross section calculated with $\log(\hat s)$ resummation and matched to fixed order for a process requiring just one jet, and our results also extend the region of resummation for processes with two jets or more. This is possible because the resummation is performed explicitly in phase space. We compare the results of our new calculation to LHC data and to next-to-leading order predictions and find a numerically significant impact of the logarithmic corrections in the shape of key distributions, which remains after normalisation of the cross section. △ Less

Submitted 16 June, 2023; v1 submitted 19 October, 2022; originally announced October 2022.

Comments: 21 pages, 11 figures; v2: plots updated with HX component, matches published version

Report number: IPPP/22/71, DCPT/22/142

All Order Merging of High Energy and Soft Collinear Resummation

Authors: Jeppe R. Andersen, Hitham Hassan, Sebastian Jaskiewicz

Abstract: We present a method of merging the exclusive LO-matched high energy resummation of High Energy Jets (HEJ) with the parton shower of Pythia which preserves the accuracy of the LO cross sections and the logarithmic accuracy of both resummation schemes across all of phase space. Predictions produced with this merging prescription are presented with comparisons to data from experimental studies and su… ▽ More We present a method of merging the exclusive LO-matched high energy resummation of High Energy Jets (HEJ) with the parton shower of Pythia which preserves the accuracy of the LO cross sections and the logarithmic accuracy of both resummation schemes across all of phase space. Predictions produced with this merging prescription are presented with comparisons to data from experimental studies and suggestions are made for further observables and experimental cuts which highlight the importance of both high energy and soft-collinear effects. △ Less

Submitted 20 January, 2023; v1 submitted 13 October, 2022; originally announced October 2022.

Comments: Proceedings of 51st International Symposium on Multiparticle Dynamics (ISMD2022)

Report number: IPPP/22/70

Object oriented data analysis of surface motion time series in peatland landscapes

Authors: Emily G. Mitchell, Ian L. Dryden, Christopher J. Fallaize, Roxane Andersen, Andrew V. Bradley, David J. Large, Andrew Sowter

Abstract: Peatlands account for 10% of UK land area, 80% of which are degraded to some degree, emitting carbon at a similar magnitude to oil refineries or landfill sites. A lack of tools for rapid and reliable assessment of peatland condition has limited monitoring of vast areas of peatland and prevented targeting areas urgently needing action to halt further degradation. Measured using interferometric synt… ▽ More Peatlands account for 10% of UK land area, 80% of which are degraded to some degree, emitting carbon at a similar magnitude to oil refineries or landfill sites. A lack of tools for rapid and reliable assessment of peatland condition has limited monitoring of vast areas of peatland and prevented targeting areas urgently needing action to halt further degradation. Measured using interferometric synthetic aperture radar (InSAR), peatland surface motion is highly indicative of peatland condition, largely driven by the eco-hydrological change in the peatland causing swelling and shrinking of the peat substrate. The computational intensity of recent methods using InSAR time series to capture the annual functional structure of peatland surface motion becomes increasingly challenging as the sample size increases. Instead, we utilize the behavior of the entire peatland surface motion time series using object oriented data analysis to assess peatland condition. In a Gibbs sampling scheme, our cluster analysis based on the functional behavior of the surface motion time series finds features representative of soft/wet peatlands, drier/shrubby peatlands and thin/modified peatlands align with the clusters. The posterior distribution of the assigned peatland types enables the scale of peatland degradation to be assessed, which will guide future cost-effective decisions for peatland restoration. △ Less

Submitted 28 September, 2022; originally announced September 2022.

Comments: 30 pages, 16 figures

A Framework for Improving the Reliability of Black-box Variational Inference

Authors: Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins

Abstract: Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet flexible alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, stochastic optimization methods for BBVI remain unreliable and require substantial expertise and hand-tuning to apply effectively. In this paper, we propose Robust and Automated Black-bo… ▽ More Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet flexible alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, stochastic optimization methods for BBVI remain unreliable and require substantial expertise and hand-tuning to apply effectively. In this paper, we propose Robust and Automated Black-box VI (RABVI), a framework for improving the reliability of BBVI optimization. RABVI is based on rigorously justified automation techniques, includes just a small number of intuitive tuning parameters, and detects inaccurate estimates of the optimal variational approximation. RABVI adaptively decreases the learning rate by detecting convergence of the fixed--learning-rate iterates, then estimates the symmetrized Kullback--Leibler (KL) divergence between the current variational approximation and the optimal one. It also employs a novel optimization termination criterion that enables the user to balance desired accuracy against computational cost by comparing (i) the predicted relative decrease in the symmetrized KL divergence if a smaller learning were used and (ii) the predicted computation required to converge with the smaller learning rate. We validate the robustness and accuracy of RABVI through carefully designed simulation studies and on a diverse set of real-world model and data examples. △ Less

Submitted 16 May, 2024; v1 submitted 29 March, 2022; originally announced March 2022.

Journal ref: Journal of Machine Learning Research, 25(219):1-71, 2024

Event Generators for High-Energy Physics Experiments

Authors: J. M. Campbell, M. Diefenthaler, T. J. Hobbs, S. Höche, J. Isaacson, F. Kling, S. Mrenna, J. Reuter, S. Alioli, J. R. Andersen, C. Andreopoulos, A. M. Ankowski, E. C. Aschenauer, A. Ashkenazi, M. D. Baker, J. L. Barrow, M. van Beekveld, G. Bewick, S. Bhattacharya, N. Bhuiyan, C. Bierlich, E. Bothmann, P. Bredt, A. Broggio, A. Buckley , et al. (187 additional authors not shown)

Abstract: We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator developme… ▽ More We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments. △ Less

Submitted 26 February, 2025; v1 submitted 21 March, 2022; originally announced March 2022.

Comments: 164 pages, 10 figures, contribution to Snowmass 2021

Report number: CP3-22-12, DESY-22-042, FERMILAB-PUB-22-116-SCD-T, IPPP/21/51, JLAB-PHY-22-3576, KA-TP-04-2022, LA-UR-22-22126, LU-TP-22-12, MCNET-22-04, OUTP-22-03P, P3H-22-024, PITT-PACC 2207, UCI-TR-2022-02

Journal ref: SciPost Phys. 16 (2024) 5, 130

Cyber-resilience for marine navigation by information fusion and change detection

Authors: Dimitrios Dagdilelis, Mogens Blanke, Rasmus Hjorth Andersen, Roberto Galeazzi

Abstract: Cyber-resilience is an increasing concern in developing autonomous navigation solutions for marine vessels. This paper scrutinizes cyber-resilience properties of marine navigation through a prism with three edges: multiple sensor information fusion, diagnosis of not-normal behaviours, and change detection. It proposes a two-stage estimator for diagnosis and mitigation of sensor signals used for co… ▽ More Cyber-resilience is an increasing concern in developing autonomous navigation solutions for marine vessels. This paper scrutinizes cyber-resilience properties of marine navigation through a prism with three edges: multiple sensor information fusion, diagnosis of not-normal behaviours, and change detection. It proposes a two-stage estimator for diagnosis and mitigation of sensor signals used for coastal navigation. Developing a Likelihood Field approach, a first stage extracts shoreline features from radar and matches them to the electronic navigation chart. A second stage associates buoy and beacon features from the radar with chart information. Using real data logged at sea tests combined with simulated spoofing, the paper verifies the ability to timely diagnose and isolate an attempt to compromise position measurements. A new approach is suggested for high level processing of received data to evaluate their consistency, that is agnostic to the underlying technology of the individual sensory input. A combined parametric Gaussian modelling and Kernel Density Estimation is suggested and compared with a generalized likelihood ratio change detector that uses sliding windows. The paper shows how deviations from nominal behaviour and isolation of the components is possible when under attack or when defects in sensors occur. △ Less

Submitted 1 February, 2022; originally announced February 2022.

Comments: 18 pages, 21 figures

ACM Class: G.3; I.2; I.4; I.5

HEJ 2.1: High-energy Resummation with Vector Bosons and Next-to-Leading Logarithms

Authors: Jeppe R. Andersen, James Black, Helen Brooks, Bertrand Ducloué, Marian Heil, Andreas Maier, Jennifer M. Smillie

Abstract: We present version 2.1 of the High Energy Jets (HEJ) event generator for hadron colliders. HEJ is a Monte Carlo generator for processes at high energies with multiple well-separated jets in the final state. To achieve accurate predictions, conventional fixed-order perturbative QCD is supplemented with an all-order resummation of large high-energy logarithms. The new version 2.1 now supports proces… ▽ More We present version 2.1 of the High Energy Jets (HEJ) event generator for hadron colliders. HEJ is a Monte Carlo generator for processes at high energies with multiple well-separated jets in the final state. To achieve accurate predictions, conventional fixed-order perturbative QCD is supplemented with an all-order resummation of large high-energy logarithms. The new version 2.1 now supports processes with final-state leptons originating from a charged or neutral vector boson together with multiple jets, in addition to processes available in earlier versions. Furthermore, the all-order resummation is extended to include an additional gauge-invariant class of subdominant logarithmic corrections. HEJ 2.1 can be obtained from https://hej.hepforge.org. △ Less

Submitted 16 May, 2022; v1 submitted 29 October, 2021; originally announced October 2021.

Comments: 16 pages, 4 figures

Report number: DCPT/21/86, DESY-21-174, IPPP/21/43, MCNET-21-14, SAGEX-21-33

Journal ref: Comp. Phys. Commun. 278, (2022) 108404

Unbiased Elimination of Negative Weights in Monte Carlo Samples

Authors: Jeppe R. Andersen, Andreas Maier

Abstract: We propose a novel method for the elimination of negative Monte Carlo event weights. The method is process-agnostic, independent of any analysis, and preserves all physical observables. We demonstrate the overall performance and systematic improvement with increasing event sample size, based on predictions for the production of a W boson with two jets calculated at next-to-leading order perturbati… ▽ More We propose a novel method for the elimination of negative Monte Carlo event weights. The method is process-agnostic, independent of any analysis, and preserves all physical observables. We demonstrate the overall performance and systematic improvement with increasing event sample size, based on predictions for the production of a W boson with two jets calculated at next-to-leading order perturbation theory. △ Less

Submitted 16 May, 2022; v1 submitted 16 September, 2021; originally announced September 2021.

Comments: 22 pages, 7 figures

Report number: DCPT/21/54, DESY 21-135, IPPP/21/27, MCNET-21-14, SAGEX-21-29

High-energy logarithmic corrections to the QCD component of same-sign W-pair production

Authors: Jeppe R. Andersen, Bertrand Ducloué, Conor Elrick, Andreas Maier, Graeme Nail, Jennifer M. Smillie

Abstract: We describe the calculation of the QCD contribution to same-sign $W$-pair production, $pp\to e^\pm ν_e μ^\pm ν_μjj$, resumming all contributions scaling as $α_W^4 α_s^{2+k}\log^k(\hat s/p_\perp^2)$ [arXiv:2107.06818]. These leading logarithmic contributions are enhanced by typical cuts used for Vector Boson Scattering (VBS) studies. We show that while the cross sections are little affected by thes… ▽ More We describe the calculation of the QCD contribution to same-sign $W$-pair production, $pp\to e^\pm ν_e μ^\pm ν_μjj$, resumming all contributions scaling as $α_W^4 α_s^{2+k}\log^k(\hat s/p_\perp^2)$ [arXiv:2107.06818]. These leading logarithmic contributions are enhanced by typical cuts used for Vector Boson Scattering (VBS) studies. We show that while the cross sections are little affected by these corrections, other more exclusive observables relevant for experimental studies are affected more significantly. △ Less

Submitted 28 July, 2021; originally announced July 2021.

Comments: DIS2021 proceedings, prepared for submission to SciPost

Third-order terahertz optical response of graphene in the presence of Rabi Oscillations

Authors: Sawsan Daws, David R. Andersen

Abstract: Graphene has been shown to exhibit a nonlinear response due to its unique band structure. In this paper, we study the terahertz (THz) response metallic armchair graphene nanoribbons, specifically current density and Rabi oscillations beyond the semiclassical Boltzman model. We performed quantum mathematical modeling by first finding a solution to the unperturbed Hamiltonian for a single Fermion in… ▽ More Graphene has been shown to exhibit a nonlinear response due to its unique band structure. In this paper, we study the terahertz (THz) response metallic armchair graphene nanoribbons, specifically current density and Rabi oscillations beyond the semiclassical Boltzman model. We performed quantum mathematical modeling by first finding a solution to the unperturbed Hamiltonian for a single Fermion in the dipole gauge and then applying a polarized, THz electrical field. After writing the solution in terms of the four eigenstates of the Dirac system, we numerically calculated the $x$ and $y$ components of the induced current density resulting from applying the terahertz electrical field. Due to the inclusion of the Rabi Oscillation in our calculation of the optical response, we predict both odd and even harmonics, as well as continuum oscillations of the power density spectrum in the THz regime. Lastly, we show a rapid decay of the power harmonics. △ Less

Submitted 24 July, 2021; originally announced July 2021.

Comments: 5 pages, 2 figures, to be published

Logarithmic corrections to the QCD component of same-sign W-pair production for VBS studies

Authors: Jeppe R. Andersen, Bertrand Ducloué, Conor Elrick, Andreas Maier, Graeme Nail, Jennifer M. Smillie

Abstract: We present the results of the first calculation of the logarithmic corrections to the QCD contribution to same-sign $W$-pair production, $pp\to e^\pm ν_e μ^\pm ν_μjj$, for same-sign charged leptons. This includes all leading logarithmic contributions which scale as $α_W^4 α_s^{2+k}\log^k(\hat s/p_\perp^2)$. This process is important for the study of electroweak couplings and hence the QCD contribu… ▽ More We present the results of the first calculation of the logarithmic corrections to the QCD contribution to same-sign $W$-pair production, $pp\to e^\pm ν_e μ^\pm ν_μjj$, for same-sign charged leptons. This includes all leading logarithmic contributions which scale as $α_W^4 α_s^{2+k}\log^k(\hat s/p_\perp^2)$. This process is important for the study of electroweak couplings and hence the QCD contributions are usually suppressed through a choice of Vector Boson Scattering (VBS) cuts. These select regions of phase space where logarithms in $\hat s/p_\perp^2$ are enhanced. While the logarithmic corrections lead to a small change for the cross sections, several distributions relevant for experimental studies are affected more significantly. △ Less

Submitted 18 May, 2022; v1 submitted 14 July, 2021; originally announced July 2021.

Comments: 20 pages, 10 figures v2: Matches published version (Dec 2021)

Report number: DCPT/21/14, DESY 21-107, IPPP/21/07, MCnet-21-13, SAGEX-21-16