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 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 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 introduces Assimilative Causal Inference (ACI), a novel framework that leverages Bayesian data assimilation to trace causes backward from observed effects, enabling the identification of dynamic, high-dimensional causal interactions and the online tracking of intermittent causal roles without requiring observations of candidate causes.
This paper introduces a novel amortized inference technique using likelihood-weighted Normalizing Flows that overcomes the limitations of standard unimodal base distributions in capturing multi-modal posteriors by initializing the flow with a Gaussian Mixture Model, thereby enabling efficient and accurate parameter estimation in high-dimensional inverse problems without requiring posterior training samples.
This paper demonstrates that a lightweight and scalable EfficientNet architecture, augmented with global jet features, achieves competitive top-quark jet tagging performance while significantly reducing computational costs compared to Transformer-based and standard Graph Neural Network approaches.
This paper proposes and validates a robust, general-purpose Bayesian method for averaging inconsistent data sets by treating input uncertainties as lower bounds to better handle outliers, demonstrating its effectiveness across various scientific domains and providing a freely available Python implementation.
This paper introduces a novel hyperbolic embedding framework for multilayer networks that simultaneously preserves global multilayer structure and enables layer-specific analysis across heterogeneous node sets, outperforming existing methods in community detection and disease-related brain region clustering.
The paper introduces GollumFit, an open-source framework designed to efficiently perform binned-likelihood analyses on IceCube neutrino telescope data by incorporating both general and specific model parameters to handle complex fitting scenarios with numerous nuisance parameters.
This paper provides practical recommendations and considerations from researchers across major particle physics experiments on adopting emerging machine learning-based unbinned unfolding techniques to replace traditional binned histogram methods for more flexible, high-dimensional data analysis.
This paper introduces IJazZ2.0, a novel analytical likelihood-based method implemented in the IJazZ software that enables computationally efficient, unbiased, and robust simultaneous extraction of lepton (and photon) energy scale and resolution corrections across multiple categories by leveraging exact smearing treatments and automatic differentiation.