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 presents an unsupervised, reconstruction-based anomaly detection method using a pedestal-trained convolutional autoencoder to efficiently extract Regions of Interest from CYGNO optical TPC images, achieving high signal retention and significant data reduction with low inference latency on consumer hardware.
This paper presents training materials and resources developed by the HEP Software Foundation to equip physicists with Apptainer containerization skills, thereby enhancing the reproducibility, portability, and collaboration of High Energy and Nuclear Physics analyses.
This paper introduces a geometric framework for adaptive whitening in gravitational-wave detectors by reformulating spectral factorization as parallel transport on a principal bundle, proving that the resulting flat connection ensures path-independent, hysteresis-free filter updates that unify static Wiener-Hopf theory with dynamic real-time control.
This paper demonstrates that combining non-negative matrix factorization for dimensionality reduction with parameter-adaptable reservoir computing enables the accurate and robust prediction of both the occurrence and precise timing of tipping points in complex spatiotemporal dynamical systems, including climate projections, while significantly reducing computational overhead.
This paper demonstrates that while both unbinned and binned analysis methods are unbiased under ideal conditions, the binned method suffers significant bias from temperature uncertainties, prompting the authors to propose a novel stability-assessment procedure to ensure robust correlation estimates in underground muon flux studies.
This paper introduces a physics-informed deep learning framework that combines multiple-instance learning with spatiotemporal modeling to overcome signal stochasticity and background interference, enabling the robust, label-free detection of single-peptide phosphorylation events using particle-in-pore plasmonic nanopores.
This paper introduces a fast and numerically stable method for local polynomial smoothing and differentiation using discrete orthogonal Chebyshev polynomials, which significantly improves computational efficiency and accuracy over standard monomial-based approaches, making it ideal for high-resolution spectral analyses like axion dark matter searches.
This paper presents a multivariate copula framework for constructing non-Gaussian weak-lensing likelihoods that better match simulated data than Gaussian approximations, finding that while this approach induces significant shifts in constraints for stage-III surveys, the effect becomes negligible for stage-IV surveys, suggesting standard Gaussian likelihoods remain sufficient for future large-scale analyses.
This paper introduces a maximum likelihood estimation method to infer burst-merging kernels from bursty time series, enabling the precise characterization of their hierarchical burst structures and underlying mechanisms through both synthetic and empirical data validation.
FluxMC is a machine learning-enhanced framework that combines Flow Matching with Parallel Tempering MCMC to overcome the computational bottlenecks of traditional Bayesian inference, enabling rapid and high-fidelity parameter estimation for space-based gravitational-wave observations without compromising between model accuracy and analysis speed.