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 establishes a statistical theory demonstrating that the maximal temporal horizon for learning in gated recurrent neural networks is determined by the interplay between the decay rate of an effective learning rate envelope and the concentration properties of heavy-tailed gradient noise, yielding distinct logarithmic, polynomial, or exponential scaling regimes for learnability.
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 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 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 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.
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 summarizes recent advancements in using constituent-based machine learning architectures, such as graph neural networks and transformers, to classify hadronic final states in the ATLAS Experiment, detailing their performance on simulated and real data while outlining future directions for data-driven and model-independent tagging strategies.
This paper introduces Randomly Modulated Gaussian Processes as a unifying framework that generalizes diverse anomalous diffusion models in heterogeneous media, enabling systematic statistical characterization, deriving key metrics for experimental classification, and facilitating biophysical interpretations of single-particle trajectories.