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 demonstrates that applying Topological Data Analysis to time delay embeddings of audio signals, specifically using delays related to fractions of the fundamental period, effectively characterizes musical timbre by revealing harmonic structures and distinguishing between instruments in both synthetic and real data.
This paper presents an LLM-based pipeline that accurately extracts multicomponent alloy data from both text and tables to create the largest publicly available database of its kind, enabling sustainable materials design by identifying high-performance alloy candidates for lightweighting, soft magnetic, and corrosion-resistant applications.
By analyzing Discrete Dislocation Dynamics simulations of fcc Cu, this study reveals that dislocation link lengths on active slip systems follow a double-exponential distribution due to stress-induced bowing, whereas inactive systems exhibit a single-exponential distribution, a distinction explained by modeling the network as a one-dimensional Poisson process with super-linear growth rates for long links.
This paper proposes a functional information framework to quantify classical objectivity in Quantum Darwinism by measuring the abundance of environment fragments that redundantly encode pointer information, revealing thermodynamic constraints where each additional bit of objectivity doubles the minimal heat dissipation required for record stabilization.
This paper presents a novel framework combining high-fidelity Monte Carlo simulations with Bayesian inference to achieve rapid, high-accuracy quantification of mobile gamma-ray sources, significantly advancing capabilities in radiological safety, geophysical mapping, and space exploration.
This paper presents an interpretable multimodal machine learning framework that integrates heterogeneous analytical data from SEM, Raman, gas adsorption, and electrical measurements to characterize carbon nanotube films, demonstrating that nonlinear models like XGBoost can accurately predict material properties while providing physically meaningful insights into the underlying structure-property relationships.
This paper presents a theoretical analysis demonstrating that unsupervised feature selection for proteins using Differentiable Information Imbalance reveals a phase transition between glass-like and liquid-like states, where the critical number of physico-chemical features coincides with the saturation of downstream classification performance, offering a principled criterion for identifying minimal feature sets.
This paper demonstrates that feature superposition in neural networks induces a universal power-law training exponent of approximately 1, independent of data statistics, thereby accelerating training dynamics by up to tenfold compared to sequential learning without superposition.
This paper introduces SPARKX, an open-source Python package designed to streamline and enhance the analysis of relativistic kinematics in heavy-ion collision experiments by providing a comprehensive, multi-format toolkit that integrates with major simulation frameworks like SMASH and JETSCAPE.
This paper demonstrates that short-range attractive interactions among thermally driven Brownian quasiparticles enable energy-efficient, scalable, and robust optimization through emergent cooperative behavior, outperforming non-interacting searchers in both static and dynamic spatial landscapes.