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 proposes a Physics-Informed Temporal U-Net that utilizes a VGG-based perceptual loss and a time-weighted parabolic boundary condition to reconstruct high-fidelity, turbulence-preserving fluid dynamics from sparse temporal observations.
ArchGEM is an automated framework that uses peak-finding and Gaussian Mixture Model clustering to identify scattered-light noise in LIGO spectrograms, allowing researchers to characterize the physical properties and motion of scattering surfaces to guide noise mitigation strategies.
The paper proposes that the stable ratio between EUV and radio brightness temperatures in the quiet-Sun corona is a manifestation of the divergence between two different Maxwellian projections of a non-equilibrium (kappa) electron distribution.
By analyzing high-Reynolds-number turbulence simulations, this study demonstrates that intermittency extends the Markov-Einstein coherence length of the energy cascade to approximately three times the canonical estimate, suggesting that the standard Markovian assumption used in cascade modeling is insufficient for describing intermittent events.
The paper introduces DySIB (Dynamical Symmetric Information Bottleneck), a method that recovers interpretable, low-dimensional dynamical state variables from high-dimensional experimental data by maximizing predictive mutual information in a latent space without requiring observation reconstruction.
This paper introduces a boundary-aware spectral method using DCT and RFFT to compute stable local statistics (such as mean and variance) on incomplete Cartesian and polar grids, effectively mitigating wrap-around artifacts and handling missing data.
This paper presents a comprehensive study of the expected precision for Higgs boson hadronic decay modes () at the FCC-ee, demonstrating that combining $ZH$ and Vector boson fusion processes with four IDEA detectors will achieve percent-to-per-mil level measurements and provide the first sensitivity to evidence the rare decay.
This paper addresses limitations in existing resonant anomaly detection methods by introducing new simulated signal benchmarks and a comprehensive "kitchen sink" observable set combining Energy Flow Polynomials and subjettiness variables, demonstrating that this approach offers superior sensitivity across diverse signal types while an attribute bagging variant significantly reduces training costs with comparable performance.
This paper systematically evaluates the design and training of scientific machine learning emulators for the MAM4 aerosol microphysics module in E3SMv2, demonstrating that effective scaling, convergence monitoring, and moderate network complexity are critical for accurately reproducing aerosol concentration changes under cloud-free conditions.
This paper proposes and evaluates two Bayesian methods, Laplace approximation and Markov Chain Monte Carlo, for quantifying the uncertainty of hybrid spectral unmixing estimators in -ray spectrometry, demonstrating that while both perform well under ideal conditions, Markov Chain Monte Carlo remains robust when spectral constraints or dominant backgrounds create non-Gaussian posterior distributions where Laplace approximation fails.