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.
This paper proposes a Bayesian optimization-based automatic termination strategy for inelastic neutron-scattering experiments that dynamically selects optimal bin widths to identify when further data collection yields diminishing returns, thereby significantly reducing beam time waste and computational search costs compared to exhaustive methods.
This paper analyzes brightness variations from the 1892–2021 outbursts of comet 17P/Holmes, particularly the 2007 mega-outburst, to constrain the size distribution and total mass of ejected porous agglomerates, thereby providing physically motivated initial conditions for modeling long-term dust-trail evolution and meteoroid stream origins.
This paper presents a high-resolution, single-camera method that combines rapid light-sheet scanning with a neural autoencoder incorporating isometricity constraints to accurately reconstruct the complex 3D deformations of transparent, crumpled sheets in flow.
This paper proposes a novel algebraic framework for gamma-ray spectroscopy by modeling decay schemes as quivers and introducing a "coincidence algebra bundle" that extends path algebras to calculate coincidence probabilities for non-composable transitions.
This paper introduces a Bayesian protocol that combines full-spectrum template matching with probabilistic evidence evaluation to successfully identify single- and two-source neutron configurations from recoil and time-of-flight spectroscopy data with high statistical significance, even at low event counts.
This paper presents an optimized protocol using artificial neural networks to identify solvents in starch-liquid slurries from post-desiccation fracture patterns with 96% accuracy by analyzing nine morphological features, particularly crack area distribution.
This paper investigates the time evolution of survival probability in Erdős-Rényi random networks, revealing that its decay and correlation hole depth are governed by the multifractal properties of eigenstates and the network's average degree.
This paper introduces \texttt{py5vec}, a modular Python package that implements and extends the 5-vector method for continuous gravitational wave searches by incorporating robust statistical improvements, enabling Bayesian parameter estimation, and validating its performance on LIGO O4a data.
This paper introduces a novel, training-free graph theory-based algorithm that accurately segments and measures 3D detonation cells from pressure traces, overcoming the limitations of traditional manual and 2D methods to provide a robust tool for analyzing complex cellular geometries in detonation research.
This paper introduces a novel learned binning method to compare wavelet-based statistics (WST and WPH) against traditional angular power spectra () for CMB and weak lensing, finding that while wavelet methods perform similarly to power spectra for CMB auto-correlations, they significantly outperform them in cross-correlations with galaxy weak lensing, particularly when using the new binning approach.