327 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 proposes a novel "Clearsky-Free" short-term solar radiation forecasting approach using Extreme Learning Machines (ELM) that directly learns from raw Global Horizontal Irradiance data to eliminate the limitations and complexities of traditional clearsky models while achieving accuracy and robust uncertainty quantification comparable to or better than existing benchmarks.
This paper presents a systematic Monte Carlo-based uncertainty propagation analysis for a spherical magnetic field camera, quantifying how sensor calibration and positioning errors impact the spatial distribution of field estimation uncertainty to identify dominant error sources and assess the robustness of spherical harmonic methods.
This paper introduces a robust framework utilizing the Stochastic Coefficient of Variation (sCV) and Forecastability (F) metrics to overcome the limitations of traditional variability measures by isolating stochastic fluctuations from deterministic trends in solar irradiance, thereby enabling refined uncertainty quantification and improved operational decision-making across multiple time scales.
This paper presents a hybrid framework that combines a physics-guided First-Order Shear Deformation Theory model with machine learning and uncertainty quantification to achieve accurate, robust, and data-efficient impact localisation and force estimation in composite plates.
The paper introduces Dara, an automated framework that utilizes exhaustive tree searches and robust Rietveld refinement to reliably identify and refine multiple phases in complex powder X-ray diffraction patterns, thereby reducing manual effort and minimizing misinterpretation in materials characterization.
This study demonstrates that passive seismic sensing combined with machine learning and topological acoustics can effectively monitor remote forests by identifying characteristic tree signatures in ambient seismic noise, offering a robust, all-weather alternative to satellite-based observation.
This paper employs a Fisher Information approach to demonstrate that External Compton models in blazars suffer from significantly higher parameter degeneracy and lower information extraction limits compared to Synchrotron Self-Compton models, particularly for FSRQs, thereby establishing the necessity of time-resolved SED modeling to effectively constrain physical parameters in EC-dominated sources.
This paper introduces a response-matrix-centred forward-folding approach as an alternative to traditional unfolding for presenting cross-section measurements, accompanied by the ReMU Python software framework to facilitate the direct comparison of model predictions with reconstructed data while robustly handling detector effects and systematic uncertainties.
This paper proposes a Bayesian estimation method to optimize the measurement window in synchrotron-radiation-based Mössbauer spectroscopy, demonstrating that this approach improves the precision of center shift measurements by more than three times compared to conventional Lorentzian fitting.
This paper proposes robust test statistics and an algorithm for inflating variances to enable conservative hypothesis testing and model parameter estimation on normally distributed data when full covariance information is unavailable.