Approximate solutions of a general stochastic velocity-jump model subject to discrete-time noisy observationsAdvances in experimental techniques allow the collection of high-resolution spatio-temporal data that track individual motile entities over time. These tracking data motivate the use of mathematical…Arianna Ceccarelli, Alexander P. Browning, Ruth E. Baker·Jun 28, 2024SaveLearn
An insightful approach to bearings-only tracking in log-polar coordinatesThe choice of coordinate system in a bearings-only (BO) tracking problem influences the methods used to observe and predict the state of a moving target. Modified Polar Coordinates (MPC) and…Athena Helena Xiourouppa, Dmitry Mikhin, Melissa Humphries et al.·Jun 28, 2024SaveLearn
A Bayesian Framework to Investigate Radiation Reaction in Strong FieldsRecent experiments aiming to measure phenomena predicted by strong field quantum electrodynamics have done so by colliding relativistic electron beams and high-power lasers. In such experiments,…E. E. Los, C. Arran, E. Gerstmayr et al.·Jun 26, 2024SaveLearn
XENONnT WIMP Search: Signal & Background Modeling and Statistical InferenceThe XENONnT experiment searches for weakly-interacting massive particle (WIMP) dark matter scattering off a xenon nucleus. In particular, XENONnT uses a dual-phase time projection chamber with a…XENON Collaboration, E. Aprile, J. Aalbers et al.·Jun 19, 2024SaveLearn
A simple tool for weighted averaging of inconsistent data setsThe weighted average of inconsistent data is a common and tedious problem that many scientists have encountered. The standard weighted average is not recommended for these cases, and various…Martino Trassinelli, Marleen Maxton·Jun 12, 2024SaveLearn
Time-series-analysis-based detection of critical transitions in real-world non-autonomous systemsReal-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient…Klaus Lehnertz·Jun 7, 2024SaveLearn
Generative Diffusion Models for Fast Simulations of Particle Collisions at CERNIn High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Machine learning simulation methods have…Mikołaj Kita, Jan Dubiński, Przemysław Rokita et al.·Jun 5, 2024SaveLearn
Mesoscopic Bayesian Inference by Solvable ModelsThe rapid advancement of data science and artificial intelligence has affected physics in numerous ways, including the application of Bayesian inference, setting the stage for a revolution in…Shun Katakami, Shuhei Kashiwamura, Kenji Nagata et al.·Jun 5, 2024SaveLearn
Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic ModelsCorrecting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods…Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin et al.·Jun 3, 2024SaveLearn
SwdFold:A Reweighting and Unfolding method based on Optimal Transport TheoryHigh-energy physics experiments rely heavily on precise measurements of energy and momentum, yet face significant challenges due to detector limitations, calibration errors, and the intrinsic nature…Chu-Cheng Pan, Xiang Dong, Yu-Chang Sun et al.·Jun 2, 2024SaveLearn
Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and ReconstructionDetector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our…Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii et al.·May 31, 2024SaveLearn
Untangling Climate's Complexity: Methodological InsightsIn this article, we review the interdisciplinary techniques (borrowed from physics, mathematics, statistics, machine-learning, etc.) and methodological framework that we have used to understand…Alka Yadav, Sourish Das, Anirban Chakraborti·May 28, 2024SaveLearn
Tracking Dynamical Transitions using Link Density of Recurrence NetworksWe present Link Density (LD) computed from the Recurrence Network (RN) of a time series data as an effective measure that can detect dynamical transitions in a system. We illustrate its use using…Rinku Jacob, R. Misra, K P Harikrishnan et al.·May 24, 2024SaveLearn
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy PhysicsExtracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorentz Geometric Algebra…Jonas Spinner, Victor Bresó, Pim de Haan et al.·May 23, 2024SaveLearn
Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experimentsArtificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient…Yeonju Go, Dmitrii Torbunov, Timothy Rinn et al.·May 23, 2024SaveLearn
Decomposing causality into its synergistic, unique, and redundant componentsCausality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for…Álvaro Martínez-Sánchez, Gonzalo Arranz, Adrián Lozano-Durán·May 20, 2024SaveLearn
Statistical divergences in high-dimensional hypothesis testing and a modern technique for estimating themHypothesis testing in high dimensional data is a notoriously difficult problem without direct access to competing models' likelihood functions. This paper argues that statistical divergences can be…Jeremy J. H. Wilkinson, Christopher G. Lester·May 10, 2024SaveLearn
Data Reduction for Low Energy Nuclear Physics Experiments Using Data FramesLow energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data…Caleb Marshall·May 9, 2024SaveLearn
Accurate estimation of the normalized mutual information of multidimensional dataWhile the linear Pearson correlation coefficient represents a well-established normalized measure to quantify the interrelation of two stochastic variables X and Y, it fails for multidimensional…Daniel Nagel, Georg Diez, Gerhard Stock·May 8, 2024SaveLearn
Physics-based linear regression for high-dimensional forward uncertainty quantificationWe introduce linear regression using physics-based basis functions optimized through the geometry of an inner product space. This method addresses the challenge of surrogate modeling with…Ziqi Wang·May 8, 2024SaveLearn
PHYSTAT Informal Review: Marginalizing versus Profiling of Nuisance ParametersThis is a writeup, with some elaboration, of the talks by the two authors (a physicist and a statistician) at the first PHYSTAT Informal review on January 24, 2024. We discuss Bayesian and…Robert D. Cousins, Larry Wasserman·Apr 26, 2024SaveLearn
Temporal scaling theory for bursty time series with clusters of arbitrarily many eventsLong-term temporal correlations in time series in a form of an event sequence have been characterized using an autocorrelation function (ACF) that often shows a power-law decaying behavior. Such…Hang-Hyun Jo, Tibebe Birhanu, Naoki Masuda·Apr 26, 2024SaveLearn
A direct method of continuous unwrapping the phase from an interferogram imageA new method recovers phase difference of interfering wavefronts from a pattern of interference fringes, avoiding discontinuity problem. The continuous phase is a solution of the first order…V. Berejnov, B. Y. Rubinstein·Apr 19, 2024SaveLearn
Reconstruction of network dynamics from partial observationsWe investigate the reconstruction of time series from dynamical networks that are partially observed. In particular, we address the extent to which the time series at a node of the network can be…Tyrus Berry, Timothy Sauer·Apr 17, 2024SaveLearn
A Mereological Approach to Higher-Order Structure in Complex Systems: from Macro to Micro with M\"obiusRelating macroscopic observables to microscopic interactions is a central challenge in the study of complex systems. While current approaches often focus on pairwise interactions, a complete…Abel Jansma·Apr 17, 2024SaveLearn