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.
SpecTUS is a deep neural network that performs *de novo* structural annotation of small molecules from low-resolution GC-EI-MS spectra, significantly outperforming traditional database search methods by achieving perfect structure reconstruction for 43% of test compounds with a single suggestion and surpassing hybrid search results in 76% of cases.
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 introduces the Ascona model, a queueing-based framework that generates continuous-time synthetic temporal networks with controllable smoothness and prescribed event patterns by combining a Markovian link process with block-structured endpoint distributions.
This paper presents a novel probabilistic framework for cislunar orbit determination that overcomes the limitations of Gauss's method in three-body dynamics by combining kinematic fitting of noisy observations for initial state estimation with a Particle Gaussian Mixture Filter for long-term uncertainty reduction.
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.