physics.data-an papers | Gist.Science

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.

Gauge-Mediated Contagion: A Quantum Electrodynamics-Inspired Framework for Non-Local Epidemic Dynamics and Superdiffusion

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.

Jose de Jesus Bernal-Alvarado, David Delepine2026-04-02🧬 q-bio

Global asteroseismology of 19,000 red giants in the TESS Continuous Viewing Zones

This paper presents a comprehensive asteroseismic catalogue of 19,151 red giants in the TESS Continuous Viewing Zones, utilizing seven years of data to achieve high-precision measurements of stellar parameters that significantly expand the known population of oscillating giants and provide valuable uniform data for Galactic Archaeology.

K. R. Sreenivas, Timothy R. Bedding, Daniel Huber, Dennis Stello, Marc Hon, Claudia Reyes, Yaguang Li, Daniel Hey2026-04-02🔭 astro-ph

Discovery of Symbolic Hamiltonian Expressions with Buckingham-Symplectic Networks

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.

Joe Germany, Joseph Bakarji, Sara Najem2026-04-02🌀 nlin

Car Dependency in Urban Accessibility

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.

Bruno Campanelli, Francesco Marzolla, Matteo Bruno, Hygor Piaget Monteiro Melo, Vittorio Loreto2026-04-02📊 stat

Principal component analysis of wavefunction snapshots in non-equilibrium dynamics

This paper proposes a principal component analysis framework for non-equilibrium quantum dynamics that maximizes information in the largest principal component to link dimensionality reduction with physical observables, demonstrated through Heisenberg spin chain simulations and applicable to higher-dimensional quantum simulators.

Dharmesh Yadav, Devendra Singh Bhakuni, Bijay Kumar Agarwalla2026-04-02⚛️ quant-ph

Simulated Performance of Timescale Metrics for Aperiodic Light Curves

This paper evaluates the effectiveness of three timescale metrics— $\Delta m$ - $\Delta t$ plots, peak-finding, and Gaussian process regression—for analyzing aperiodic light curves through simulations, finding that while Gaussian process regression struggles with noise and irregular sampling, the other two methods can coarsely characterize timescales across a broad parameter space.

Krzysztof Findeisen, Ann Marie Cody, Lynne Hillenbrand2026-04-01🔭 astro-ph

Inhomogeneous random graphs with infinite-mean fitness variables

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.

Luca Avena, Diego Garlaschelli, Rajat Subhra Hazra, Margherita Lalli2026-04-01🔬 cond-mat

The distribution of violent event and interevent times in conflicts

Despite a mathematical theorem predicting that fine-grained data should reveal lognormal distributions for violent event intervals, empirical analysis shows that the commonly observed power law distribution remains valid, indicating that violent events are energy-intensive processes.

Jeroen Bruggeman2026-04-01🔬 physics

Observing cities as a complex system

This paper reviews urban scaling theory as a framework for analyzing cities as complex systems while arguing that focusing solely on population size overlooks critical intra-city disparities, necessitating a deeper exploration of variations within individual urban areas.

Rafael Prieto-Curiel2026-04-01🔬 physics

Energy Time Ptychography for one-dimensional phase retrieval

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.

Ankita Negi, Leon Merten Lohse, Sven Velten, Ilya Sergeev, Olaf Leupold, Sakshath Sadashivaiah, Dimitrios Bessas, Aleksandr Chumakhov, Christina Brandt, Lars Bocklage, Guido Meier, Ralf Röhlsberger2026-04-01🔬 physics.atom-ph

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