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.
This paper presents an iterative AI framework that autonomously proposes, evaluates, and refines dynamical dark-energy equations of state, successfully identifying novel phenomenological parameterizations that outperform traditional models in Bayesian evidence when tested against cosmological observations.
This paper presents a Bayesian framework for optimal and adaptive experimental design to estimate non-linear parameters in the presence of additive white Gaussian noise, specifically by marginalizing over linear parameters and demonstrating its application to exponentially decaying signals in contexts such as nuclear magnetic resonance and solid-state spin sensor relaxometry.
This paper investigates the key features and surprising statistical properties of deep learning from a physics-informed perspective, with a specific focus on neural scaling laws and their interplay with the inductive biases relevant to physics problems.
This paper presents the first experimental measurement of Quantum First-Passage-Time Distributions (QFPTDs) using a single trapped ion, achieved through a novel composite-phase laser pulse sequence that enables tunable stroboscopic projective measurements and opens new avenues for exploring quantum dynamics and measurement problems.
This paper introduces MiniFool, a physics-constraint-aware adversarial attack algorithm that minimizes a cost function combining a test statistic with target score deviation to evaluate the robustness of deep neural networks in particle and astroparticle physics, as demonstrated through applications on MNIST, IceCube tau neutrino data, and CMS Open Data.
This study introduces a novel framework that embeds intracranial EEG-derived brain connectivity networks into a low-dimensional Euclidean space to define a dimensionless biomarker capable of accurately distinguishing preictal seizure states from interictal periods, thereby offering a promising approach for real-time seizure risk forecasting.
This paper presents a supervised machine learning approach for automating nuclide identification in HPGe gamma spectrometry, which achieves a superior F1 score of 0.97 compared to traditional software (0.84) and utilizes SHAP values to confirm that the model relies on physically relevant photopeaks for its predictions.
This paper demonstrates that leveraging the MICrONS program's co-registered anatomical and functional data to initialize and constrain recurrent neural networks significantly enhances their performance on cognitive tasks while promoting biologically plausible organizational principles.
This paper demonstrates that multicollinearity in multiple linear regression undermines physical interpretability by amplifying weight fluctuations and generating oscillatory patterns across correlated features, a phenomenon that can be mitigated but not fully resolved by Ridge regularization.
This paper establishes a calibrated measurement framework for quantifying short-range exclusion in 2D spatial point processes by correcting the Brody exponent's baseline from 0.96 (instead of the inappropriate 1D Poisson value) and validating a strong empirical correlation between the exponent and the effective hard-core radius across diverse datasets including manufactured surfaces, prime number embeddings, and fractal structures.