316 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 presents a data-driven comparative analysis of various statistical distance metrics and normalizing functions using electron and photon events from a decaying Kr-83 isotope to evaluate the stability of a dimensionless Parameter of Interest under different conditions.
This study identifies multiplicative noise on the membrane potential as the most detrimental factor to spiking neural network performance due to neuronal silencing, and proposes a sigmoid-based input pre-filtering strategy to effectively mitigate noise-induced accuracy degradation while highlighting the networks' greater robustness to common noise.
This paper introduces an exact-approximate Markov Chain Monte Carlo method utilizing a novel unbiased Poisson likelihood estimator to achieve exact statistical inferences from noisy Monte Carlo simulations in collider physics, offering robust results at a computational cost comparable to existing approximate methods.
This paper demonstrates that the OmniLearned particle physics foundation model, pre-trained on high-energy collision data, can be effectively transferred to low-energy fixed-target neutrino experiments to outperform models trained from scratch on tasks like energy regression and pion classification, thereby validating the potential for detector-agnostic inference across vastly different energy scales and physics processes.
This paper demonstrates that simulation-based inference can reliably extract physical parameters from the noisy spectral signatures of analogue black holes, offering a powerful new tool for probing curved-spacetime physics in laboratory gravity simulators.
This paper experimentally demonstrates a quantum anomaly detection algorithm using a three-qubit solid-state spin processor in diamond, achieving a 15.4% error rate in identifying anomalous audio-encoded quantum states after training on normal samples.
This study utilizes US census data to demonstrate that parenthood and marriage fundamentally reshape urban mobility patterns, revealing significant variations in how different cities accommodate specific household types and advocating for tailored urban planning strategies that move beyond one-size-fits-all models.
This paper proposes a hierarchical maximum likelihood estimation method based on a Bayesian model that improves the accuracy and uncertainty propagation of time-resolved NMR data analysis for metabolic monitoring, outperforming traditional two-stage procedures and Fourier methods in both high-field and micronscale experimental setups.
This paper proposes a graph-based method using pulsar timing residuals to detect the stochastic gravitational wave background by analyzing network structural characteristics, demonstrating its ability to identify signals with a strain amplitude of and providing weak evidence for an SGWB in the NANOGrav 15-year dataset.
This paper introduces a computational workflow that utilizes recent advances in nonlinear dynamics to algorithmically identify multistable states in high-dimensional climate and exoplanet data, optimize key observables for monitoring, and quantify the "intermingledness" of alternative steady states and their basins of attraction.