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.
The paper introduces JetPrism, a configurable Conditional Flow Matching framework that addresses the misleading nature of standard training losses in nuclear physics simulations by establishing a multi-metric evaluation protocol to ensure generative models achieve true physical fidelity and convergence beyond generic loss indicators.
By integrating the complete synaptic and neuropeptidergic connectomes of *C. elegans* within a unified multiplex framework grounded in statistical physics, this study reveals how synaptic and extrasynaptic signaling form four complementary communication regimes that collectively optimize brain function for speed, modulation, robustness, and survival.
QuantumXCT is a hybrid quantum-classical generative framework that infers cell-cell communication by learning interaction-induced state transformations via parameterized quantum circuits, enabling the de novo discovery of complex regulatory networks and communication hubs without relying on pre-curated ligand-receptor databases.
This paper proposes a Template-Adapted Mixture Model that leverages multiple biased simulations to perform robust, data-driven parameter inference for signal fraction estimation, effectively mitigating simulation-data mismodeling in complex particle physics analyses.
This paper introduces a novel gauge-mediated epidemiological framework inspired by Quantum Electrodynamics that replaces direct contact with a pathogen field to naturally model non-local dynamics and superdiffusion, successfully deriving standard SIR equations as a mean-field limit while using 1-loop fluctuations and real-world COVID-19 data to reveal early warning signals, spatial shielding effects, and a phase-transition-based condition for outbreaks.
The paper introduces BuSyNet, a deep learning architecture that integrates dimensional consistency and symplectic geometry to discover interpretable, closed-form symbolic Hamiltonian expressions, achieving superior long-term prediction accuracy and stability on physical systems like the harmonic oscillator and Kepler problem compared to state-of-the-art methods.
This study introduces a novel Car Dependency Index to quantify accessibility gaps across 18 cities, revealing that systemic, network-level transit expansions are essential to dismantle car-based systems and achieve equitable, sustainable urban mobility.
This paper mathematically analyzes an inhomogeneous Erdős-Rényi random graph model driven by infinite-mean Pareto-distributed vertex fitness variables, characterizing the asymptotic behavior of typical degrees, correlations, and local clustering while identifying a phase transition for the existence of isolated vertices.
This paper introduces "Energy Time Ptychography," a novel one-dimensional phase retrieval method that utilizes multiple energetically overlapping nuclear forward scattering measurements of synchrotron X-ray pulses to simultaneously reconstruct transmission spectra and scattering phases, thereby overcoming the bandwidth limitations of traditional gamma-ray sources.
This paper introduces "The Closure Challenge," a standardized open-source benchmark comprising curated datasets, evaluation code, and specific test cases designed to establish a common metric for evaluating and advancing machine learning models in Reynolds-averaged Navier-Stokes turbulence modeling.