Observational causality by states and interaction type for scientific discoveryCausality plays a central role in understanding interactions between variables in complex systems. These systems often exhibit state-dependent causal relationships, where both the strength and…Álvaro Martínez-Sánchez, Adrián Lozano-Durán·May 16, 2025SaveLearn
Comparative Analysis of Richardson-Lucy Deconvolution and Data Unfolding with Mean Integrated Square Error OptimizationTwo maximum likelihood-based algorithms for unfolding or deconvolution are considered: the Richardson-Lucy method and the Data Unfolding method with Mean Integrated Square Error (MISE) optimization…Nikolay D. Gagunashvili·May 15, 2025SaveLearn
Contrastive Normalizing Flows for Uncertainty-Aware Parameter EstimationEstimating physical parameters from data is a crucial application of machine learning (ML) in the physical sciences. However, systematic uncertainties, such as detector miscalibration, induce data…Ibrahim Elsharkawy, Yonatan Kahn·May 13, 2025SaveLearn
A Centrality-independent Framework for Revealing Genuine Higher-Order Cumulants in Heavy-Ion CollisionsWe propose a novel centrality definition-independent method for analyzing higher-order cumulants, specifically addressing the challenge of volume fluctuations that dominate in low-energy heavy-ion…Zhaohui Wang, Xiaofeng Luo·May 6, 2025SaveLearn
Correct Estimation of Higher-Order Spectra: From Theoretical Challenges to Practical Multi-Channel Implementation in SignalSnapHigher-order spectra (Brillinger's polyspectra) offer powerful methods for solving critical problems in signal processing and data analysis. Despite their significant potential, their practical…Markus Sifft, Armin Ghorbanietemad, Fabian Wagner et al.·May 2, 2025SaveLearn
Data-driven Approach for Interpolation of Sparse DataStudies of hadron resonances and their properties are limited by the accuracy and consistency of measured datasets, which can originate from many different experiments. We have used Gaussian…R. F. Ferguson, D. G. Ireland, B. McKinnon·May 2, 2025SaveLearn
Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural NetworksThe growing luminosity frontier at the Large Hadron Collider is challenging the reconstruction and analysis of particle collision events. Increased particle multiplicities are straining latency and…William Sutcliffe, Marta Calvi, Simone Capelli et al.·Apr 30, 2025SaveLearn
When Should Team KPIs Be Absolute or Relative for Match-Outcome Prediction?In rugby union and association football, team key performance indicators (KPIs) can be represented in absolute terms or relative to the opponent. Relativisation sometimes improves match-outcome…M. R. Brown, G. Scott, L. Kilduff·Apr 28, 2025SaveLearn
Modular Aggregation as a Debiasing Method for Non-Stationary Discrete Sources: Convergence and Numerical ValidatioWe analyze modular aggregation---summing N independent outcomes modulo m---as a post-processing method for extracting nearly uniform randomness from biased discrete sources. Using discrete…Eduardo Gueron·Apr 23, 2025SaveLearn
Machine-Learning-Based Method for Goodness-of-Fit Test in Amplitude AnalysisPurpose: Amplitude analysis is a pivotal tool in hadron spectroscopy, fundamentally involving a series of likelihood fits to multi-dimensional experimental distributions. While robust…Huoyi Hou, Beijiang Liu·Apr 23, 2025SaveLearn
Testing models for angular power spectra: A distribution-free approachA novel goodness-of-fit strategy is introduced for testing models of angular power spectra with unknown parameters. Using this strategy, it is possible to assess the validity of such models without…Sara Algeri, Xiangyu Zhang, Erik Floden et al.·Apr 22, 2025SaveLearn
Unbinned Inference with Correlated EventsModern machine learning has enabled parameter inference from event-level data without the need to first summarize all events with a histogram. All of these unbinned inference methods make use of the…Krish Desai, Owen Long, Benjamin Nachman·Apr 18, 2025SaveLearn
Maximum Information Extraction Via Clustering and Minimization of Shannon EntropyIn the analysis of any type of system, granting maximum information extraction from its data is non-trivial. Confidence in successful information extraction typically builds on prior knowledge of the…Matteo Becchi, Giovanni Maria Pavan·Apr 17, 2025SaveLearn
Transforming Simulation to Data Without PairingWe explore a generative machine learning-based approach for estimating multi-dimensional probability density functions (PDFs) in a target sample using a statistically independent but related control…Eli Gendreau-Distler, Luc Le Pottier, Haichen Wang·Apr 15, 2025SaveLearn
Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold TechniqueThe choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section modeling uncertainties…Roger G. Huang, Andrew Cudd, Masaki Kawaue et al.·Apr 9, 2025SaveLearn
Hybrid Random Concentrated Optimization Without Convexity AssumptionWe propose a new random method to minimize deterministic continuous functions over subsets S of high-dimensional space RK without assuming convexity. Our procedure alternates…Pierre Bertrand, Michel Broniatowski, Wolfgang Stummer·Mar 31, 2025SaveLearn
DUNE Software and Computing Research and DevelopmentThe international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the…DUNE Collaboration, A. Abed Abud, R. Acciarri et al.·Mar 31, 2025SaveLearn
Maximum likelihood estimation of burst-merging kernels for bursty time seriesVarious time series in natural and social processes have been found to be bursty. Events in the time series rapidly occur within short time periods, forming bursts, which are alternated with long…Tibebe Birhanu, Hang-Hyun Jo·Mar 19, 2025SaveLearn
Ordinal language of antipersistent binary walksThis paper explores the effectiveness of using ordinal pattern probabilities to evaluate antipersistency in the sign decomposition of long-range anti-correlated Gaussian fluctuations. It is…Felipe Olivares·Mar 14, 2025SaveLearn
Data augmentation using diffusion models to enhance inverse Ising inferenceIdentifying model parameters from observed configurations poses a fundamental challenge in data science, especially with limited data. Recently, diffusion models have emerged as a novel paradigm in…Yechan Lim, Sangwon Lee, Junghyo Jo·Mar 13, 2025SaveLearn
SPARKX: A Software Package for Analyzing Relativistic Kinematics in Collision ExperimentsSPARKX is an open-source Python package developed to analyze simulation data from heavy-ion collision experiments. By offering a comprehensive suite of tools, SPARKX simplifies data analysis…Nils Sass, Hendrik Roch, Niklas Götz et al.·Mar 12, 2025SaveLearn
Estimating complete basis set extrapolation error through random walkWe propose a method of estimating the uncertainty of a result obtained through extrapolation to the complete basis set limit. The method is based on an ensemble of random walks which simulate all…Jakub Lang, Michał Przybytek, Michał Lesiuk·Mar 12, 2025SaveLearn
Machine Learning for Single-Ended Event Reconstruction in PROSPECT ExperimentThe Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its…M. Andriamirado, A. B. Balantekin, C. D. Bass et al.·Mar 9, 2025SaveLearn
Signatures of extreme events in the cumulative entropic spectrumIn this study, the cumulative effect of the empirical probability distribution of a random variable is identified as a factor that amplifies the occurrence of extreme events in datasets. To quantify…Ewa A. Drzazga-Szczȩśniak, Adam Z. Kaczmarek, Marta Kielak et al.·Mar 9, 2025SaveLearn
A skeletonization based image segmentation algorithm to isolate slender regions in 3D microstructuresThe work proposes an image segmentation algorithm that isolates slender regions in three-dimensional microstructures. Characterizing slender regions in material microstructures is an extremely…Vinit Vijay Deshpande, Romana Piat·Mar 7, 2025SaveLearn