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 demonstrates that when the prior given hyperparameters in a Bayesian hierarchical model is a canonical maximum entropy distribution, the resulting dependent marginal prior also possesses a maximum entropy property, but with respect to a constraint on the marginal distribution of a function of the unknown parameters, thereby clarifying the implicit information assumptions of such models.
This paper presents a scalable, two-step framework that propagates chemical kinetics parameter uncertainties onto reduced manifolds to enable efficient, spatially resolved uncertainty quantification in high-fidelity reacting flow simulations, revealing significant variability in trajectory and equilibrium times driven by mixing and low-to-intermediate temperature chemistry.
This study introduces the enhanced WT-RDF+ framework, which leverages machine learning-assisted parameter tuning to overcome amplitude accuracy limitations in reconstructing Radial Distribution Functions for amorphous Ge-Se and Ag-Ge-Se systems, thereby outperforming standard ML benchmarks even with limited training data.
This paper introduces a robust, automated method using Integral Field Spectroscopy to disentangle AGN and star-formation contributions in local galaxies, revealing a moderate correlation between AGN accretion rates and recent nuclear star formation that supports the co-evolution of these processes.
This paper presents an overview of GenomeBits, a physics-inspired signal analysis tool that utilizes Discrete Fourier Transform and quantum-based extensions to map nucleotide sequences into numerical signals, successfully revealing intrinsic genomic features, mutation patterns, and unique "order-disorder" transitions in viral genomes such as SARS-CoV-2 variants and Monkeypox.
This paper demonstrates that utilizing unconventionally large learning rates to induce transient chaotic dynamics during neural network training creates an optimal balance between exploration and exploitation, thereby accelerating convergence to high accuracy across various architectures and tasks.
This paper develops a thermodynamic framework for asymptotic inference that maps statistical concepts like Shannon information and parameter variance to thermodynamic entropy and state variables, revealing fundamental limits on information gain and unifying ensemble and inferential physics as opposing shadow processes.
This paper demonstrates that Graph Neural Networks outperform traditional models like TabNet in estimating muon particle momentum for the CMS experiment, highlighting the critical role of node feature dimensionality and the benefits of leveraging the data's inherent graph structure to improve trigger system efficiency.
This paper proposes a physics-informed Bayesian latent diffusion model that integrates probabilistic encoding, diffusion dynamics, and physics constraints to achieve stable and reliable anomaly detection for new physics searches in collider data.
This paper introduces Extracted Mode Tracking (EMT), an unsupervised machine learning framework that resolves gravity-capillary wave evolution in vessels with unknown boundary conditions by directly extracting wave modes from spatio-temporal data, thereby enabling quantitative analysis of nonlinear dynamics without requiring prior theoretical modeling.