316 papers
This section explores the intersection where physics meets data analysis, a rapidly evolving frontier where complex datasets reveal hidden patterns in the universe. From tracking particle collisions to modeling cosmic structures, these studies rely on advanced statistical methods to turn raw numbers into fundamental insights about how reality works.
Gist.Science monitors every new preprint in this category as it appears on arXiv, ensuring you never miss a breakthrough. We process each entry to provide both plain-language overviews for general understanding and detailed technical summaries for experts, bridging the gap between dense research and clear comprehension.
Below are the latest papers in physics and data analysis, organized for easy reading and discovery.
This paper introduces a novel early warning signal called DE-AC, which estimates the dominant eigenvalue from autocorrelation functions to reliably predict the onset of period-doubling bifurcations in cardiac systems with superior sensitivity and specificity compared to existing indicators.
This paper introduces TRec, a physics-informed muon scattering tomography framework that significantly enhances the detection of missing fuel in sealed microreactor cores by reconstructing curved muon trajectories and incorporating momentum measurements, thereby outperforming conventional methods like PoCA in both sensitivity and speed under realistic cosmic-ray conditions.
This tutorial presents a computationally efficient, two-step Bayesian framework for quantifying uncertainty in linear shock compression data by deriving posterior distributions for model parameters and propagating them through Rankine-Hugoniot equations to generate multiple consistent Hugoniot curves, offering a more robust and interpretable alternative to traditional least squares and bootstrapping methods.
The paper introduces Noise2Ghost, a self-supervised deep learning method that achieves superior noise reduction and reconstruction quality in ghost imaging without requiring clean reference data, thereby enabling high-quality imaging in low-light scenarios such as dose-sensitive x-ray fluorescence and biological studies.
This paper introduces a physics-embedded Bayesian neural network (PE-BNN) framework that integrates an energy-independent phenomenological shell factor and WAIC-based hyperparameter optimization to accurately predict energy-dependent fission product yields with fine structures and close agreement with known nuclear shell effects.
This paper introduces Structured Kolmogorov-Arnold Neural ODEs (SKANODEs), a framework that combines structured state-space modeling with Kolmogorov-Arnold Networks to accurately recover interpretable physical latent states and discover compact symbolic governing equations for nonlinear dynamical systems, outperforming black-box neural ODEs and classical identification methods across synthetic and real-world datasets.
This study challenges the prevailing hypothesis that rotational acceleration is the primary cause of concussion by demonstrating through direct in vivo measurements that linear acceleration is a significantly more precise predictor of injury, leading to the development of a liquid shock-absorbing helmet technology that could reduce concussion risk by up to 73%.
This paper introduces the Global Settlement Percolation (GSP) dataset, which characterizes global settlement configurations by quantifying the critical distance at which isolated settlements merge into a single cluster across various spatial scales to provide an independent measure of connectivity for urban, economic, and ecological research.
This paper proposes a practical framework for analyzing extreme values in finite, multivariate, and correlated systems—demonstrated through high-frequency finance data—by rotating returns into the correlation matrix's eigenbasis to isolate collective and idiosyncratic effects, thereby enabling the use of univariate peaks-over-threshold methods to estimate tail risks while accounting for nonstationarity.
This study utilizes open multifrequency astronomical data to construct visual and auditory representations of nine blazars via MIDI and Parameter Mapping Sonification, demonstrating how this multimodal approach effectively reveals variability patterns and promotes inclusive scientific communication.