323 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 demonstrates that high-accuracy, reference-free material classification using sparse-frequency terahertz spectroscopy can be achieved by applying data-driven feature selection algorithms to identify discriminative absorption bands, thereby eliminating the need for broadband sources and reference measurements for compact sensor applications.
This paper introduces a data-driven protocol that segments non-stationary, non-Gaussian, and heteroskedastic weather data into local pseudo-equilibrium seasons to construct accurate low-dimensional models using state-based generalized master equations, thereby overcoming the limitations of standard generalized Langevin equations in driven, dissipative systems.
This study demonstrates that internal coupling significantly broadens the operational regime and enhances the performance of quantum Otto cycles across equilibrium and non-equilibrium limit cycles, enabling engine or refrigerator functionality in previously inoperable parameter ranges and allowing efficiencies to exceed standard Otto bounds while remaining below the Carnot limit.
This paper introduces a novel "Hilbert entropy" methodology that combines Hilbert curve-based dimension reduction with generalized entropy measures to effectively quantify the complexity of high-dimensional physical systems, demonstrating its efficacy in accurately identifying phase transitions and revealing linear relationships with fractal dimensions.
This paper introduces MJOLNIR, a generalized software package designed to reduce, visualize, and analyze the complex data generated by novel multiplexing triple-axis neutron spectrometers, such as the CAMEA instrument, to facilitate improved data acquisition rates and collaborative advancements in multi-dimensional data modeling.
The paper introduces DMCPy, a modular Python-based software package designed to streamline data reduction, visualization, and analysis for both powder and single-crystal neutron diffraction experiments conducted on the upgraded DMC diffractometer at SINQ.
This paper presents a novel algorithm that determines the directionality of 2D discrete data distributions by rotating a reference matrix to minimize the Frobenius norm of the difference, a process generalized via a derived continuous analog (CFND) that approximates the relationship as an absolute sine function for applications in fields like neutrino detection and astronomy.
This paper introduces AMBER, a segmentation algorithm that leverages the rotational independence of background signals to automatically decompose high-dimensional neutron scattering data into foreground and background contributions, thereby reducing expert input and systematic errors in multiplexing spectrometers.
This paper introduces and experimentally validates "Dichography," a method that algorithmically separates overlapping diffraction signals from two time-delayed, multi-color X-ray pulses to reconstruct two distinct ultrafast images of nanoscale samples from a single detector pattern, thereby enabling the capture of structural dynamics before significant radiation damage occurs.
This paper introduces Geometric Autoencoders for Bayesian Inversion (GABI), a framework that learns geometry-aware generative models from large datasets of varying physical systems to serve as informative priors for robust, well-calibrated uncertainty quantification in ill-posed inverse problems without requiring knowledge of governing equations.