Physics and Computing Performance of the EggNet Tracking PipelineParticle track reconstruction is traditionally computationally challenging due to the combinatorial nature of the tracking algorithms employed. Recent developments have focused on novel algorithms…Jay Chan, Brandon Wang, Paolo Calafiura·Jun 3, 2025SaveLearn
On the dynamical evolution of randomness Part B: Geometrisation and the origin of convergence in LLNIn classical probability theory, the convergence of empirical frequencies to theoretical probabilities: as captured by the Law of Large Numbers (LLN): is treated as axiomatic and emergent from…Allen Lobo·Jun 3, 2025SaveLearn
Brightify: A tool for calculating directionally-resolved brightness in neutron sourcesBrightness is a critical metric for optimizing the design of neutron sources and beamlines, yet there is no direct way to calculate brightness within most Monte Carlo packages used for neutron source…Mina Akhyani, Luca Zanini, Henrik Rønnow·May 28, 2025SaveLearn
Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging TrendsBayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis of complex…Wang-Ji Yan, Lin-Feng Mei, Yuan-Wei Yin et al.·May 28, 2025SaveLearn
MoreFit: A More Optimised, Rapid and Efficient FitParameter estimation via unbinned maximum likelihood fits is a central technique in particle physics. This article introduces MoreFit, which aims to provide a more optimised, rapid and efficient…Christoph Langenbruch·May 18, 2025SaveLearn
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
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
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 use…Markus Sifft, Armin Ghorbanietemad, Fabian Wagner et al.·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
Relative Advantage: Quantifying Performance in Noisy Competitive SettingsPerformance measurement in competitive domains is frequently confounded by shared environmental factors that obscure true performance differences. For instance, absolute metrics can be heavily…M. R. Brown, G. Scott, L. Kilduff·Apr 28, 2025SaveLearn
Modular Debiasing: A Robust Method for Quantum Randomness ExtractionWe propose a novel modular debiasing technique applicable to any discrete random source, addressing the fundamental challenge of reliably extracting high-quality randomness from inherently imperfect…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