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 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 demonstrates that a team of large language model (LLM) agents can autonomously generate code to solve complex particle physics data analysis tasks, specifically anomaly detection on the LHC Olympics dataset, achieving performance comparable to human state-of-the-art results.
This paper identifies non-normal eigenvector amplification in multidimensional random multiplicative systems as a distinct and dominant mechanism for generating power-law distributions and criticality, where the non-orthogonality of eigenvectors induces transient growth that increases the effective Lyapunov exponent and lowers the tail exponent, particularly as system dimension increases.
This study utilizes archival GPS carrier phase data to conduct a retrospective search for exotic low-mass fields emitted by the GW170817 binary neutron star merger, finding no significant signal but establishing new 95% confidence-level constraints on the interaction energy scale of quadratic couplings that improve upon existing astrophysical limits.
This paper introduces a hybrid deep learning framework combining an autoencoder and Gaussian processes to model the Point Spread Function (PSF) across the full field of view with higher accuracy than the current state-of-the-art PIFF method, thereby addressing critical limitations for weak gravitational lensing analyses in major cosmological surveys like LSST.
This paper demonstrates that likelihood-free Simulation-Based Inference, particularly the Neural Posterior Estimation method, offers a computationally efficient and accurate alternative to traditional MCMC for exploring high-dimensional Beyond the Standard Model parameter spaces, successfully inferring posterior distributions for both 5-parameter and 9-parameter pMSSM scenarios including dark matter constraints.
This paper proposes a general Simulation-Based Inference framework utilizing Factorizable Normalizing Flows and an amortized training strategy to efficiently profile systematic uncertainties while simultaneously extracting multivariate distributions of interest, overcoming the computational bottlenecks of traditional unbinned likelihood fits.
This paper proposes a Bayesian sequential approach for time-lapse Full Waveform Inversion using Hamiltonian Monte Carlo to effectively quantify uncertainties in high-dimensional seismic problems by integrating baseline survey data as prior knowledge, demonstrating accuracy comparable to parallel schemes while managing computational costs.
This paper derives the stationary distributions of an extended Chiarella financial model across different regimes, identifying specific conditions for unimodal and bimodal distributions and correcting previous misconceptions regarding the relationship between trend and mispricing bifurcations.
This paper proposes a methodology to infer unknown parameters and reconstruct hidden macroscopic dynamics of finite-size neural networks by synchronizing a known mean-field model with a single scalar observable using a differential evolution algorithm.