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 quantifies the impact of various Geant4 electromagnetic physics constructors on the accuracy of energy deposition and computational performance for rare event searches using CaWO and Ge targets, aiming to guide the selection of optimal simulation configurations for background prediction.
This paper presents a distillation framework that compresses complex, state-of-the-art eSPA ensemble forecasts of ENSO phase into compact, interpretable models, thereby preserving high predictive skill while enabling rigorous diagnostics of the spatiotemporal dynamics and physical precursors driving long-range ENSO predictability.
This paper introduces DA-HASC, a data-driven sequential framework that reconstructs high-dimensional system states via data assimilation and quantifies structural complexity changes using manifold learning and Von Neumann entropy to detect tipping points from noisy, partial observations.
This paper presents the design and application of a wide-surface furnace capable of performing high-temperature, in situ X-ray diffraction and fluorescence on 100 mm combinatorial material libraries, enabling the rapid calculation of thermal expansion coefficients and revealing limitations of Vegard's law in high-entropy systems.
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.
This paper proposes a machine-learning approach using autoencoder-based clustering to detect nonequilibrium phase transitions in open quantum systems by analyzing space-time trajectories from continuous monitoring, thereby bypassing the need for extensive projective measurements required to estimate quantum states.
This paper proposes a robust causal discovery method for real-world time series that leverages the power-law distribution of frequency spectra to amplify genuine causal signals, thereby outperforming existing algorithms in noisy environments across various domains.
This paper presents a memristive tabular variational autoencoder implemented on an analog content-addressable memory (ACAM) device that achieves real-time, 12x compression of high-energy physics calorimeter data with ultra-low latency (24 ns) and high throughput (330M compressions/s).
This paper introduces a scalable, convex optimization-based information-matching approach using the Fisher Information Matrix to select optimal training data that specifically constrains parameters relevant to downstream quantities of interest, thereby enabling precise predictions with minimal data across diverse scientific fields and active learning applications.
This paper proposes a novel scale-free network model driven by the competition between degree and betweenness centralities, which reveals a new "stars-with-filament" structural class, a tunable phase diagram encompassing 47 real-world networks, and unique scaling behaviors for average degree and path length.