Distinguishing subsampled power laws from other heavy-tailed distributionsDistinguishing power-law distributions from other heavy-tailed distributions is challenging, and this task is often further complicated by subsampling effects. In this work, we evaluate the…Silja Sormunen, Lasse Leskelä, Jari Saramäki·Apr 15, 2024SaveLearn
The CMS statistical analysis and combination tool: COMBINEThis paper describes the COMBINE software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined…CMS Collaboration·Apr 9, 2024SaveLearn
A feature-based information-theoretic approach for detecting interpretable, long-timescale pairwise interactions from time seriesQuantifying relationships between components of a complex system is critical to understanding the rich network of interactions that characterize the behavior of the system. Traditional methods for…Aria Nguyen, Oscar McMullin, Joseph T. Lizier et al.·Apr 9, 2024SaveLearn
Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge ForecastingHurricanes and coastal floods are among the most disastrous natural hazards. Both are intimately related to storm surges, as their causes and effects, respectively. However, the short-term…Patrick Ebel, Brandon Victor, Peter Naylor et al.·Apr 5, 2024SaveLearn
Physics Event Classification Using Large Language ModelsThe 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America. This workshop brought together researchers from physics, data science and…Cristiano Fanelli, James Giroux, Patrick Moran et al.·Apr 5, 2024SaveLearn
The Emergence of the Normal Distribution in Deterministic Chaotic MapsThe Central Limit Theorem states that, in the limit of a large number of terms, an appropriately scaled sum of independent random variables yields another random variable whose probability…Damián H. Zanette, Inés Samengo·Apr 4, 2024SaveLearn
Trajectory analysis through entropy characterization over coded representationAny continuous curve in a higher dimensional space can be considered a trajectory that can be parameterized by a single variable, usually taken as time. It is well known that a continuous curve can…Roxana Peña-Mendieta, Ania Mesa-Rodríguez, Ernesto Estevez-Rams et al.·Apr 4, 2024SaveLearn
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality MethodsThis survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science. More specifically, the paper presents an overview of…Sahara Ali, Uzma Hasan, Xingyan Li et al.·Apr 3, 2024SaveLearn
An inversion problem for optical spectrum data via physics-guided machine learningWe propose the regularized recurrent inference machine (rRIM), a novel machine-learning approach to solve the challenging problem of deriving the pairing glue function from measured optical spectra.…Hwiwoo Park, Jun H. Park, Jungseek Hwang·Apr 3, 2024SaveLearn
Deep Probabilistic Direction Prediction in 3D with Applications to Directional Dark Matter DetectorsWe present the first method to probabilistically predict 3D direction in a deep neural network model. The probabilistic predictions are modeled as a heteroscedastic von Mises-Fisher distribution on…Majd Ghrear, Peter Sadowski, Sven Einar Vahsen·Mar 23, 2024SaveLearn
Normalizing Flows for Domain Adaptation when Identifying Hyperon EventsThis study focuses on the novel application of a normalizing flow as a method of domain adaptation. Normalizing flows offer a way to transform data points between two different distributions. The…Rowan Kelleher, Anselm Vossen·Mar 21, 2024SaveLearn
Characterizing unstructured data with the nearest neighbor permutation entropyPermutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing…Leonardo G. J. M. Voltarelli, Arthur A. B. Pessa, Luciano Zunino et al.·Mar 19, 2024SaveLearn
Visualization for physics analysis improvement and applications in BESIIIModern particle physics experiments usually rely on highly complex and large-scale spectrometer devices. In high energy physics experiments, visualization helps detector design, data quality…Zhi-Jun Li, Ming-Kuan Yuan, Yun-Xuan Song et al.·Mar 19, 2024SaveLearn
Graph Neural Network for Neutrino Physics Event ReconstructionLiquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential…V Hewes, Adam Aurisano, Giuseppe Cerati et al.·Mar 18, 2024SaveLearn
A Statistical Method for Improving Momentum Measurement of Photon Conversions Reconstructed from Single ElectronsThe reconstruction of photon conversions is importantin order to improve the reconstruction efficiency of the physics measurements involving photons. However, there are significant number of…Ahmet Bingül, Zekeriya Uysal·Mar 18, 2024SaveLearn
Process signature-driven high spatio-temporal resolution alignment of multimodal dataWe present HiRA-Pro, a novel procedure to align, at high spatio-temporal resolutions, multimodal signals from real-world processes and systems that exhibit diverse transient, nonlinear stochastic…Abhishek Hanchate, Himanshu Balhara, Vishal S. Chindepalli et al.·Mar 11, 2024SaveLearn
Entropy corrected geometric Brownian motionThe geometric Brownian motion (GBM) is widely employed for modeling stochastic processes, yet its solutions are characterized by the log-normal distribution. This comprises predictive capabilities of…Rishabh Gupta, Ewa A. Drzazga-Szczȩśniak, Sabre Kais et al.·Mar 10, 2024SaveLearn
End-to-End Analysis Automation over Distributed Resources with Luigi Analysis WorkflowsIn particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo production. However, physicists performing data analyses are usually…Marcel Rieger·Feb 28, 2024SaveLearn
Data Unfolding with Mean Integrated Square Error OptimizationExperimental data in Particle and Nuclear physics, Particle Astrophysics and Radiation Protection Dosimetry are obtained from experimental facilities comprising a complex array of sensors,…Nikolay D. Gagunashvili·Feb 20, 2024SaveLearn
Quantifying Systematic Uncertainties in Experimental Physics: An Approximation MethodIn the domain of physics experiments, data fitting is a pivotal technique for extracting insights from both experimental and simulated datasets. This article presents an approximation method designed…Lu Li·Feb 15, 2024SaveLearn
"Layer-by-layer" Unsupervised Clustering of Statistically Relevant Fluctuations in Noisy Time-series Data of Complex Dynamical SystemsComplex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in unsupervised way the…Matteo Becchi, Federico Fantolino, Giovanni M. Pavan·Feb 12, 2024SaveLearn
Disentangling high order effects in the transfer entropyTransfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias - either in deficiency or excess - during both pairwise and conditioned…Sebastiano Stramaglia, Luca Faes, Jesus M. Cortes et al.·Feb 5, 2024SaveLearn
Response Theory via Generative Score ModelingWe introduce an approach for analyzing the responses of dynamical systems to external perturbations that combines score-based generative modeling with the Generalized Fluctuation-Dissipation Theorem…Ludovico Theo Giorgini, Katherine Deck, Tobias Bischoff et al.·Feb 1, 2024SaveLearn
Analysis of neutron time-of-flight spectra with a Bayesian unfolding methodologyWe have developed an innovative methodology for obtaining the neutron energy distribution from a time-of-flight (TOF) measurement based on the iterative Bayesian unfolding method and accurate Monte…A. Pérez de Rada Fiol, D. Cano-Ott, T. Martínez et al.·Jan 30, 2024SaveLearn
Limits to extreme event forecasting in chaotic systemsPredicting extreme events in chaotic systems, characterized by rare but intensely fluctuating properties, is of great importance due to their impact on the performance and reliability of a wide range…Yuan Yuan, Adrian Lozano Duran·Jan 29, 2024SaveLearn