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.
Using bias-corrected 20CRv2c reanalysis data from 1876 to 2011, this study demonstrates that human-caused climate change, primarily through rising temperatures and decreasing humidity, has driven a statistically significant increase in the mean and extreme severity of Australian fire weather conditions over the past century.
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 proposes an augmented convolutional neural network (A-CNN) model that significantly enhances the sensitivity of liquid xenon time projection chambers to neutrinoless double-beta decay by achieving over 60% background rejection while maintaining 90% signal acceptance, thereby improving the projected sensitivity of experiments like XENONnT by approximately 40%.
This paper demonstrates that the pervasive appearance of gamma distributions across diverse physical systems arises naturally from large deviation theory when Padé approximants are used to respect positivity constraints, offering a mechanism-free explanation for this universality as a constrained analog to Gaussian universality.
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.
The paper introduces the Ising noise filter, a portable, physics-informed graph-based algorithm that maps detector hits to binary spins to efficiently suppress background noise and improve track reconstruction in particle detectors, achieving high recall rates in both neutrino telescope and collider experiments.
This paper presents a data-driven LSTM surrogate model capable of learning nonlinear mappings from wave-vessel time series to accurately reproduce both parametric roll episodes and their associated statistical shifts, utilizing training data from either physical experiments or high-fidelity simulations to enhance operability and risk assessment.
This study addresses short-term atmospheric turbulence forecasting by comparing statistical and deep learning models, demonstrating that a novel normalizing flow approach (FloTS) achieves the optimal balance between predictive accuracy and well-calibrated uncertainty for robust decision-making.
This paper presents a quantum machine learning approach using rotationally symmetric quanvolutional neural networks to classify track-like and shower-like topologies in LArTPC neutrino events, finding that while these quantum models outperform similarly sized classical networks, they are ultimately surpassed by classical models with significantly more parameters.
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.