261 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 proposes a unified framework that resolves the tension between entropy growth and the emergence of complex structures by demonstrating how coarse-grained spatial ordering can coexist with increasing phase-space complexity through a geometric transport approach that links deformation tensors, nonlocal interactions, and Landau--Ginzburg self-organization, with applications extending from cosmological structure formation to broader mesoscopic systems.
This paper proposes a variational dimension-lifting framework that constructs an invertible transformation to map nonlinear stochastic state-space models into higher-dimensional linear Gaussian systems, enabling robust tracking via standard linear inference techniques while maintaining accuracy and stability in challenging regimes.
This paper introduces GAPE, a genetic algorithm-powered evolution method that optimizes deep learning models for the PROSPECT experiment, achieving a nearly 2.8-fold improvement in signal-to-background ratio for reactor antineutrino identification while addressing and mitigating time-dependent training biases.
This article introduces a Special Issue on Symbolic Regression for the Physical Sciences, summarizing its conceptual foundations, diverse applications, methodological challenges, and future directions in uncovering interpretable mathematical relationships from data.
This paper introduces a physics-informed neural operator framework that robustly estimates frequency-dependent surface admittance of locally reacting sound absorbers directly from noisy near-field measurements by embedding governing acoustic equations into the training process, thereby enabling accurate in situ characterization without explicit forward modeling.
This study introduces a Bayesian framework using simulation-based inference and neural networks to accurately estimate frequency-dependent acoustic surface impedances from sparse interior sound pressure measurements, overcoming the limitations of conventional methods and enabling robust, uncertainty-quantified characterization of complex real-world environments.
This paper presents an unsupervised, reconstruction-based anomaly detection method using a pedestal-trained convolutional autoencoder to efficiently extract Regions of Interest from CYGNO optical TPC images, achieving high signal retention and significant data reduction with low inference latency on consumer hardware.
This paper presents training materials and resources developed by the HEP Software Foundation to equip physicists with Apptainer containerization skills, thereby enhancing the reproducibility, portability, and collaboration of High Energy and Nuclear Physics analyses.
This paper introduces a physics-informed deep learning framework that combines multiple-instance learning with spatiotemporal modeling to overcome signal stochasticity and background interference, enabling the robust, label-free detection of single-peptide phosphorylation events using particle-in-pore plasmonic nanopores.
This paper presents a multivariate copula framework for constructing non-Gaussian weak-lensing likelihoods that better match simulated data than Gaussian approximations, finding that while this approach induces significant shifts in constraints for stage-III surveys, the effect becomes negligible for stage-IV surveys, suggesting standard Gaussian likelihoods remain sufficient for future large-scale analyses.