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 introduces Chain of Symbolic Regression (CoSR), a novel framework that mimics the progressive, hierarchical nature of scientific discovery to overcome the limitations of conventional end-to-end symbolic regression by successfully uncovering interpretable physical laws and improving scaling theories across diverse fluid dynamics and engineering applications.
This paper introduces a dynamical mean-field framework to analyze Ising machines on the Sherrington-Kirkpatrick model, revealing their constant-time convergence to near-optimal solutions and providing a general method for designing optimized parameter schedules, particularly highlighting the superiority of temperature-only annealing for Coherent Ising Machines.
This paper demonstrates that Dynamic Mode Decomposition (DMD) is an effective data-driven method for extracting accurate modal parameters from linear mechanical systems, provided measurement errors are kept relatively small, as validated through both theoretical analysis and experimental application to a cantilevered beam.
This paper demonstrates that a single ensemble of random transfer-matrix products in dimensions serves as a unified generator for all canonical KPZ one-point fluctuation laws (including Tracy-Widom GUE, GOE, GSE, and Baik-Rains distributions) through different geometric contractions, while also revealing distinct fluctuation statistics in intrinsic matrix observables like the leading eigenvalue.
This paper introduces a robust, real-time machine learning framework using a one-dimensional convolutional neural network to efficiently and accurately analyze Nitrogen-Vacancy center ODMR spectra, outperforming conventional nonlinear fitting in speed and reliability—particularly at low signal-to-noise ratios—as demonstrated in intracellular temperature sensing and superconducting vortex imaging.
This paper investigates the integer quantum Hall transition in open Chalker–Coddington networks by demonstrating that maximal wave-function amplitudes decompose into a log-normal gain factor and an intrinsic extreme component, revealing that while the raw extremes follow a parabolic scaling, the normalized intrinsic component resists standard generalized extreme-value collapse, thereby establishing extreme observables as a robust probe for correlated criticality in open quantum systems.
This paper mathematically proves that abstract, disentangled representations of latent variables are guaranteed to emerge at all global minima in feedforward neural networks trained on tasks dependent on those variables, offering a unified explanation for such representations observed in both biological and artificial systems.
This paper proposes a modified scattering cross section for semi-classical simulations that reconciles the mutually exclusive Pryce-Ward and Klein-Nishina statistical descriptions of entangled annihilation photons by treating them as separate entities while preserving their quantum correlations.
The paper introduces MorphZ, a post-processing estimator that leverages the Morph approximation of probability densities to provide accurate, low-cost marginal likelihood estimates from posterior samples across diverse applications, effectively resolving failures and improving results where standard methods struggle.
This paper presents a trajectory-based Koopman method that utilizes signature kernels to encode finite-time history in sea surface temperature data, enabling improved multi-year forecasting and the identification of coherent spectral modes through kernel extended dynamic mode decomposition.