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 study introduces a facet-resolved adsorption energy distribution framework utilizing machine-learned force fields to analyze 1.4 million adsorption sites across diverse alloy surfaces, thereby identifying specific compositions and orientations that optimize both activity and methanol selectivity for CO hydrogenation.
This contribution presents a scalable, convex optimization-based information alignment approach that leverages the Fisher information matrix to select minimal, high-quality training data for accurately predicting quantities of interest, thereby addressing data scarcity and parameter non-identifiability in diverse scientific modeling and active learning applications.
This study employs Bayesian leave-one-out cross-validation on pulsar-timing-array data to compare four models of supermassive-black-hole-binary evolution, finding that while current evidence does not decisively favor any single model over others, the data support ultralight-dark-matter-induced low-frequency suppression without yet distinguishing it from generic environmental hardening scenarios.
This article presents D-MODD, the first data-driven stochastic model in continuous time derived from longitudinal social media data, which accurately describes real opinion dynamics on polarized topics using a Langevin-like equation with empirically reconstructed drift and diffusion functions.
This paper introduces Partial Effective Information Decomposition (PEID), a novel interventionist framework that uniquely decomposes multivariate causal influences into unique and synergistic components under maximum-entropy interventions, thereby enabling the characterization of synergistic causation, downward causation, and interpretable causal structures in complex systems.
This paper introduces OmniMol, a state-of-the-art machine-learned interatomic potential for small molecules that leverages a Point-Edge Transformer architecture and knowledge transfer from high-energy physics to achieve excellent performance with minimal fine-tuning and uniquely fast inference.
This paper presents an open-source visual analytics workbench that enables scientists to interpret, validate, and explore embedding-based representations of large-scale weather and climate data by linking latent-space search results back to their physical origins and metadata, thereby facilitating a discovery workflow for identifying and retrieving analog events like tropical cyclones.
This paper addresses the interpretability challenges of Kolmogorov-Arnold networks (KANs) caused by high-curvature oscillations by deriving a novel basis-agnostic curvature penalty that, when applied, significantly smooths model activations without sacrificing predictive accuracy.
This paper introduces a machine learning framework that utilizes convolutional neural networks trained on response function observables to significantly enhance the classification of gravitational wave signals for testing general relativity, achieving a 33-fold improvement in sensitivity over standard waveform inputs and successfully detecting deviations in massive gravity theories.
This study proposes a robust predictive framework for groundwater heavy metal pollution in the Densu Basin that integrates Gaussian copula transformations with nested cross-validated ensemble machine learning to overcome the limitations of conventional methods and accurately model the skewed Heavy Metal Pollution Index.