TPCpp-10M: Simulated proton-proton collisions in a Time Projection Chamber for AI Foundation ModelsScientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by…Shuhang Li, Yi Huang, David Park et al.·Sep 6, 2025SaveLearn
Fluid dynamics meet network science: two cases of temporal network eigendecompositionTemporal networks, defined as sequences of time-aggregated adjacency matrices, sample latent graph dynamics and trace trajectories in graph space. By interpreting each adjacency matrix as a different…Lucas Lacasa·Sep 3, 2025SaveLearn
Confidence intervals for the Poisson distributionThe Poisson probability distribution is frequently encountered in physical science measurements. In spite of the simplicity and familiarity of this distribution, there is considerable confusion among…Frank C. Porter·Sep 2, 2025SaveLearn
Numerical study of high-dimensional covariance estimation and localization for data assimilationCovariance localization is a critical component of ensemble-based data assimilation (DA) and many current localization schemes simply dampen correlations as a function of distance. Increases in…Shay Gilpin, Matthias Morzfeld, Kevin K. Lin·Aug 22, 2025SaveLearn
Log Gaussian Cox Process Background Modeling in High Energy PhysicsBackground modeling is one of the most critical components in high energy physics data analyses, and for smooth backgrounds it is often performed by fitting using an analytic functional form. In this…Yuval Frid, Liron Barak, Pavani Jairam et al.·Aug 15, 2025SaveLearn
Copula-based analytical results of horizontal visibility graphs for correlated time seriesThe visibility graph (VG) algorithm and its variants have been extensively studied in the time series analysis as they transform the time series into the network of nodes and links, enabling to…Jeong-Min Lee, Hang-Hyun Jo·Aug 12, 2025SaveLearn
Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous ArchitecturesAs the particle physics community needs higher and higher precisions in order to test our current model of the subatomic world, larger and larger datasets are necessary. With upgrades scheduled for…Fotis I. Giasemis·Aug 10, 2025SaveLearn
Setting the Standard: Recommended Practices for Data Preprocessing in Data-Driven Climate PredictionArtificial intelligence (AI) - and specifically machine learning (ML) - applications for climate prediction across timescales are proliferating quickly. The emergence of these methods prompts a…Jason C. Furtado, Maria J. Molina, Marybeth C. Arcodia et al.·Aug 9, 2025SaveLearn
Jet Image Tagging Using Deep Learning: An Ensemble ModelJet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and…Juvenal Bassa, Vidya Manian, Sudhir Malik et al.·Aug 9, 2025SaveLearn
Error Breakdown and Sensitivity Analysis of Dynamical Quantities in Markov State ModelsMarkov state models (MSMs) are widely employed to analyze the kinetics of complex systems. But despite their effectiveness in many applications, MSMs are prone to systematic or statistical errors,…Yehor Tuchkov, Luke Evans, Sonya M. Hanson et al.·Aug 8, 2025SaveLearn
Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuningPrecise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution…A. Gavrikov, A. Serafini, D. Dolzhikov et al.·Jul 31, 2025SaveLearn
Towards a Large Physics BenchmarkWe introduce a benchmark framework developed by and for the scientific community to evaluate, monitor and steer large language model development in fundamental physics. Building on philosophical…Kristian G. Barman, Sascha Caron, Faegheh Hasibi et al.·Jul 29, 2025SaveLearn
Beyond Classical Models: Statistical Physics Tools for the Analysis of Time Series in Modern Air TransportWithin the continuous endeavour of improving the efficiency and resilience of air transport, the trend of using concepts and metrics from statistical physics has recently gained momentum. This…Felipe Olivares, Massimiliano Zanin·Jul 28, 2025SaveLearn
Neural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite CatalystsUnderstanding and predicting the activity of oxide perovskite catalysts for the oxygen evolution reaction (OER) requires descriptors that are both accurate and physically interpretable. While…Yeming Xian, Xiaoming Wang, Yanfa Yan·Jul 16, 2025SaveLearn
Inference of a time delay in stochastic systemsTime delay is ubiquitous in many experimental and real-world situations. It is often unclear whether time delay plays a significant role in observed phenomena, and if it does, how long the time lag…Robin A. Kopp, Sabine H. L. Klapp, Deepak Gupta·Jul 14, 2025SaveLearn
Physics-guided impact localisation and force estimation in composite plates with uncertainty quantificationPhysics-guided approaches offer a promising path toward accurate and generalisable impact identification in composite structures, especially when experimental data are sparse. This paper presents a…Dong Xiao, Zahra Sharif-Khodaei, M. H. Aliabadi·Jul 13, 2025SaveLearn
Similarity networks of ordinal-pattern transitions classify falling paper trajectoriesPaper fragments in free fall constitute a simple yet paradigmatic mechanical system exhibiting remarkably complex motions. Despite a long history of investigation, this system has defied…Angelo A. Flores, Leonardo G. J. M. Voltarelli, Andre S. Sunahara et al.·Jul 12, 2025SaveLearn
Mind the Gap: Navigating Inference with Optimal Transport MapsMachine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics, much of this progress has relied on excellent…Malte Algren, Tobias Golling, Francesco Armando Di Bello et al.·Jul 9, 2025SaveLearn
Functional Renormalization for Signal Detection: Dimensional Analysis and Dimensional Phase Transition for Nearly Continuous Spectra Effective Field TheorySignal detection in high dimensions is a critical challenge in data science. While standard methods based on random matrix theory provide sharp detection thresholds for finite-rank perturbations,…Riccardo Finotello, Vincent Lahoche, Dine Ousmane Samary·Jun 30, 2025SaveLearn
Investigation of the performance of a GNN-based b-jet tagging method in heavy-ion collisionsBeauty-tagged jets (b-jets)-collimated sprays of particles originating from the fragmentation of beauty quarks produced in the initial hard scatterings-provide a unique probe of parton dynamics in…Changhwan Choi, Sanghoon Lim·Jun 28, 2025SaveLearn
Algorithm to extract direction in 2D discrete distributions and a continuous Frobenius normIn this study, we present a novel algorithm for determining directionality in 2D distributions of discrete data. We compare a reference dataset with a known direction to a measured dataset with an…Jeffrey G. Yepez, Jackson D. Seligman, Max A. A. Dornfest et al.·Jun 20, 2025SaveLearn
Transition of AI Models in dependence of noiseWe investigate the dependence of the score on noise in the data, and on the network size. As a result, we obtain the so-called "cognition transition" from good performance to zero with increasing…Thomas Seidler, Markus Abel·Jun 20, 2025SaveLearn
Transfer entropy for finite dataTransfer entropy is a widely used measure for quantifying directed information flows in complex systems. While the challenges of estimating transfer entropy for continuous data are well known, it has…Alec Kirkley·Jun 19, 2025SaveLearn
The Elastic Analysis Facility's (EAF's) Contribution to the Future of Analysis at Multi-Experiment Institutions and Future CollidersThe Elastic Analysis Facility (EAF) hosted at Fermi National Accelerator Laboratory (Fermilab) is a platform being developed with the goal of providing a fast and efficient facility for physics…Elise Chavez, Maria Acosta-Flechas, Christophe Bonnaud et al.·Jun 9, 2025SaveLearn
Fuzzy permutation time irreversibility for nonequilibrium analysis of complex systemPermutation time irreversibility is an important method to quantify nonequilibrium characteristics of complex systems; however, ordinal pattern is a coarse-graining alternative of temporal structure…Wenpo Yao·Jun 9, 2025SaveLearn