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 demonstrates how machine learning-based simulation techniques can systematically optimize precision-theory-compatible observables, specifically identifying that isosceles right-triangle energy 3-point correlators maximize sensitivity to the top quark mass while remaining computable to high precision.
This paper investigates unexpected anomalies in CMS experiment data identified by the OmniLearned foundation model, finding that while background estimates perform well in validation regions, they fail to accurately model the signal region, prompting a call for further scrutiny.
This paper introduces a novel, likelihood-free framework combining Flow Matching and Simulation-Based Inference with a differentiable N-body code to jointly infer the Milky Way's gravitational potential and the progenitor properties of the GD-1 stellar stream, successfully capturing complex dynamical couplings that traditional methods struggle to model.
This study analyzes three years of high-resolution pedestrian data at a train station to reveal that strangers frequently imitate the route choices of the person directly ahead, even at the cost of longer travel times, creating bursty collective dynamics driven by local social imitation rather than rational optimization.
This paper introduces a training-free, analytical forecasting operator for cyclostationary energy processes that leverages temporal symmetry and local phase-aligned correlations to outperform classical persistence models while preserving periodic statistical properties.
This paper presents two complementary machine learning strategies for the CYGNO dark matter experiment: an unsupervised convolutional autoencoder that efficiently reduces data volume by isolating signal regions from noise, and a weakly supervised Classification Without Labels (CWoLa) framework that successfully identifies nuclear-recoil-like topologies without requiring event-level labels.
This study develops and evaluates a machine learning framework using Warn-on-Forecast System output to predict 2-6 hour severe weather probabilities, demonstrating that histogram gradient-boosted tree and U-Net models outperform traditional updraft helicity baselines, with the former achieving superior metrics and the latter providing smoother spatial guidance.
The paper introduces NGCG, a neural-symbolic pipeline that reliably discovers conservation laws from diverse dynamical systems with perfect accuracy and zero false positives by decoupling dynamics learning from symbolic extraction and employing strict validation filters.
This paper introduces a novel data-driven framework for discovering stochastic differential equations by unifying Weak SINDy's spatial Gaussian test functions with stochastic system identification, thereby eliminating structural regression bias through unbiased noise projection and enabling the joint sparse recovery of drift and diffusion terms with high accuracy across multiple benchmarks.
This paper constructs a global function designed for the simultaneous fitting of correlated energy-dependent cross sections by incorporating correlations between different processes and energy points, as well as uncertainties from integrated luminosity and center-of-mass energy measurements.