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 details the statistical methods used to detect astrometric binary asteroids in Gaia FPR data, presenting an updated list of 343 candidates and demonstrating the method's reliability through performance evaluations that show significantly higher detection rates compared to noise-only simulations.
This paper proposes a novel signal detection framework that bypasses the need to estimate the full background distribution by utilizing a single "compensator" parameter to account for background uncertainty, thereby simplifying inference complexity and improving uncertainty propagation.
This paper introduces Lumina, a modular Python-based framework that unifies fragmented multiscale material data for extreme aero-chemo-thermo-mechanical regimes into a centralized, AI-augmented ecosystem to streamline experimental design, validate chemical behaviors, and enhance predictive modeling for advanced defense and aerospace applications.
By analyzing high-resolution data from 142 cities worldwide, this study demonstrates that intra-urban climate fluctuations in temperature and air pollution follow universal scaling functions determined by average street network properties, thereby overcoming the limitations of traditional city-size metrics and enabling more accurate reduced-complexity models for urban planning.
This paper introduces a transparent, encoding-agnostic framework that uses resource counts and hardware benchmarks to demonstrate that achieving early quantum utility for the Capacitated Vehicle Routing Problem (CVRP) is currently unlikely on NISQ devices, revealing a massive qubit advantage for higher-order encodings over direct QUBO mappings while suggesting that innovative problem decomposition is essential for future quantum advantage.
This paper analytically characterizes how different hidden unit activation functions in Restricted Boltzmann Machines influence the statistics of induced interactions and the ability to learn complex, higher-order data structures, demonstrating that rapidly increasing nonlinearities like the Exponential function can significantly facilitate the representation and learning of such patterns.
GenL is a flexible, extensible, and open-source MATLAB-based program that utilizes a genetic algorithm to simulate and fit X-ray reflectivity and diffraction data from epitaxial thin films, offering both source code and pre-compiled binary options for extracting structural parameters like strain profiles and crystal roughness.
This paper introduces *vega-mir*, an open-source Python toolkit for symbolic music analysis featuring nine information-theoretic metrics, and demonstrates its utility through case studies revealing a correlation between harmonic graph centrality and harmonic distance across composers, as well as evidence that Glenn Gould's rubato is characterized by structured periodicity rather than metronomic rigidity.
This paper presents a Bayesian inference approach applied to a three-microphone tube method with circumferential microphone distribution to resolve phase jumps and accurately estimate the characteristic impedance and propagation coefficient of porous media across extended frequency ranges.
This paper proposes a hybrid neural simulation-based inference approach to constrain the Higgs trilinear self-coupling and other SMEFT operators using off-shell Higgs production at the High-Luminosity LHC, achieving near-theoretical-optimal sensitivity by combining matrix-element-enhanced training with classification-based background estimation.