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 introduces a novel, distribution-free goodness-of-fit strategy for testing angular power spectrum models with unknown parameters, which ensures broad applicability and significant computational efficiency by eliminating the need for case-by-case simulations.
This paper presents a model-free framework utilizing escort-weighted Shannon entropy and various divergence matrices to sensitively detect phase transitions and statistical changes in scattering and imaging data without requiring explicit physical models or order parameters.
By applying Allan deviation to sentence embeddings, this study characterizes the semantic dynamics of text as a stochastic trajectory, revealing distinct short-time scaling regimes that differentiate creative from technical writing and exposing a systematic reduction in long-term stability horizon in large language models compared to human cognition.
This paper introduces the Adaptive Particle Batch Smoother (AdaPBS), a novel cryospheric data assimilation algorithm that combines particle methods with the AMIS iterative framework to mitigate ensemble collapse and dynamically adjust computational costs, demonstrating superior or comparable performance against existing methods across diverse snow depth assimilation scenarios.
This paper presents a scalable AI-driven method using satellite imagery and the Segment Anything Model to automatically measure pedestrian crossing distances across America's 100 largest cities, revealing that older cities tend to have wider, more car-centric streets with median crossing distances ranging from 32 to 78 feet.
This paper introduces the data-driven ARGOS metric, a theoretically grounded and empirically robust tool for selecting the most sensitive anomaly detection models in a model-agnostic way, demonstrating its superiority over existing metrics like binary cross-entropy loss in tasks such as hyperparameter tuning and feature selection.
McSAS3 is a refactored Monte Carlo software suite featuring a graphical user interface that enables automated and flexible form-free analysis of small-angle scattering data for both dilute and dense scatterers.
The paper introduces PanopTag, a novel encoder-decoder architecture that simultaneously tags all jets in a particle collision event by leveraging inter-jet correlations and event-level context, thereby significantly outperforming traditional single-jet classification methods in heavy-flavor tagging.
To address the "boiling frog" risk of students disengaging from the epistemic practices of physics learning due to generative AI, this paper proposes an instructional design framework that positions AI as a bounded epistemic partner within cognitively activated activities to ensure students remain the primary agents of prediction, interpretation, and evaluation.
This tutorial provides a practical guide to Single Pixel Imaging and Compressive Sensing, detailing experimental implementations of various reconstruction methods—from deterministic algorithms to deep learning—along with accompanying Python notebooks to facilitate the reproduction of results and application to diverse imaging scenarios.