Under-coverage in high-statistics counting experiments with finite MC samplesWe consider the problem of setting confidence intervals on a parameter of interest from the maximum-likelihood fit of a physics model to a binned data set with a large number of bins, large…Cristina-Andreea Alexe, Joshua Bendavid, Lorenzo Bianchini et al.·Jan 19, 2024SaveLearn
Quantitative Selection of Sample Structures in Small-Angle Scattering Using Bayesian MethodsSmall-angle scattering (SAS) is a key experimental technique for analyzing nano-scale structures in various materials.In SAS data analysis, selecting an appropriate mathematical model for the…Yui Hayashi, Shun Katakami, Shigeo Kuwamoto et al.·Jan 19, 2024SaveLearn
Visualizing driving forces of spatially extended systems using the recurrence plot frameworkThe increasing availability of highly resolved spatio-temporal data leads to new opportunities as well as challenges in many scientific disciplines such as climatology, ecology or epidemiology. This…Maik Riedl, Norbert Marwan, Jürgen Kurths·Jan 18, 2024SaveLearn
Regime change detection in irregularly sampled time seriesIrregular sampling is a common problem in palaeoclimate studies. We propose a method that provides regularly sampled time series and at the same time a difference filtering of the data. The…Norbert Marwan, Deniz Eroglu, Ibrahim Ozken et al.·Jan 18, 2024SaveLearn
Machine learning approach to detect dynamical states from recurrence measuresWe integrate machine learning approaches with nonlinear time series analysis, specifically utilizing recurrence measures to classify various dynamical states emerging from time series. We implement…Dheeraja Thakur, Athul Mohan, G. Ambika et al.·Jan 18, 2024SaveLearn
Accurate Memory Kernel Extraction from Discretized Time Series DataMemory effects emerge as a fundamental consequence of dimensionality reduction when low-dimensional observables are used to describe the dynamics of complex many-body systems. In the context of…Lucas Tepper, Benjamin Dalton, Roland R. Netz·Jan 17, 2024SaveLearn
Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energyData-driven methods (DDMs), such as deep neural networks, offer a generic approach to integrated data analysis (IDA), integrated diagnostic-to-control (IDC) workflows through data fusion (DF), which…Zhehui Wang, Shanny Lin, Miles Teng-Levy et al.·Jan 16, 2024SaveLearn
Mini-jet Clustering Algorithm Using Transverse-momentum Seeds in High-energy Nuclear CollisionsWe propose an algorithm to detect mini-jet clusters in high-energy nuclear collisions, by selecting a high-transverse-momentum (pT) particle as a seed and assigning a clustering radius (R) in…Hanpu Jiang, Nanxi Yao, Cheuk-Yin Wong et al.·Jan 12, 2024SaveLearn
Building a Life Cycle Assessment Model using Bayesian NetworksThis paper introduces the Oilfield Pollutant Graphical Model (OPGM), an innovative approach designed to improve the benchmarking and uncertainty analysis of greenhouse gas (GHG) emissions in…Cedric Fraces Gasmi, Wennan Long·Jan 11, 2024SaveLearn
Learning effective good variables from physical dataWe assume that a sufficiently large database is available, where a physical property of interest and a number of associated ruling primitive variables or observables are stored. We introduce and test…Giulio Barletta, Giovanni Trezza, Eliodoro Chiavazzo·Jan 10, 2024SaveLearn
Information Flow Rate for Cross-Correlated Stochastic ProcessesCausal inference seeks to identify cause-and-effect interactions in coupled systems. A recently proposed method by Liang detects causal relations by quantifying the direction and magnitude of…Dionissios T. Hristopulos·Jan 10, 2024SaveLearn
Velocity-based sparse photon clustering for space debris ranging by single-photon LidarSingle-photon Lidar (SPL) offers unprecedented sensitivity and time resolution, which enables Satellite Laser Ranging (SLR) systems to identify space debris from distances spanning thousands of…Xialin Liu, Jia Qiang, Genghua Huang et al.·Jan 8, 2024SaveLearn
Hyperspectral shadow removal with Iterative Logistic Regression and latent Parametric Linear Combination of GaussiansShadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In…Core Francisco Park, Maya Nasr, Manuel Pérez-Carrasco et al.·Dec 24, 2023SaveLearn
Kinematic Characterization of Micro-Mobility Vehicles During Evasive ManeuversThere is an increasing need to comprehensively characterize the kinematic performances of different Micromobility Vehicles (MMVs). This study aims to: 1) characterize the kinematic behaviors of…Paolo Terranova, Shu-Yuan Liu, Sparsh Jain et al.·Dec 22, 2023SaveLearn
Generative Models for Simulation of KamLAND-ZenThe next generation of searches for neutrinoless double beta decay (0etaeta) are poised to answer deep questions on the nature of neutrinos and the source of the Universe's…Z. Fu, C. Grant, D. M. Krawiec et al.·Dec 22, 2023SaveLearn
Machine Learning for Anomaly Detection in Particle PhysicsThe detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected events that may be…Vasilis Belis, Patrick Odagiu, Thea Klæboe Årrestad·Dec 20, 2023SaveLearn
Model adaptive phase space reconstructionPhase space reconstruction (PSR) methods allow for the analysis of low-dimensional data with methods from dynamical system theory, but their application to prediction models, like those from machine…Jayesh M. Dhadphale, K. Hauke Kraemer, Maximilian Gelbrecht et al.·Dec 20, 2023SaveLearn
Leveraging the Urysohn Lemma of Topology for an Enhanced Binary ClassifierIn this article we offer a comprehensive analysis of the Urysohn's classifier in a binary classification context. It utilizes Urysohn's Lemma of Topology to construct separating functions, providing…Ernesto Lopez Fune·Dec 19, 2023SaveLearn
Drawing a better understanding of flood quantiles from a bagThe "100-year flood" is commonly used, for instance in newspapers, but flood hazard assessment is more complex than it seems. We first describe an animation entitled "bag of floods" to make flood…Christine Poulard, Renard Benjamin, Gonzalez-Sosa Enrique et al.·Dec 18, 2023SaveLearn
Signal significance incorporating systematic uncertainty for continuous testTo properly estimate signal significance while accounting for both statistical and systematic uncertainties, we conducted a study to analyze the impact of typical systematic uncertainties, such as…Yi Ding, Weiming Song, Kai Zhu·Dec 17, 2023SaveLearn
Improving new physics searches with diffusion models for event observables and jet constituentsWe introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC. By training diffusion models on side-band data, we show how background templates for the…Debajyoti Sengupta, Matthew Leigh, John Andrew Raine et al.·Dec 15, 2023SaveLearn
Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problemsData-driven methods of model identification are able to discern governing dynamics of a system from data. Such methods are well suited to help us learn about systems with unpredictable evolution or…Gina Vasey, Daniel Messenger, David Bortz et al.·Dec 8, 2023SaveLearn
Application of deep learning to the estimation of normalization coefficients in diffusion-based covariance modelsVariational data assimilation in ocean models depends on the ability to model general correlation operators in the presence of coastlines. Grid-point filters based on diffusion operators are widely…Folke K Skrunes, Mayeul Destouches, Anthony Weaver et al.·Dec 8, 2023SaveLearn
High Pileup Particle Tracking with Object CondensationRecent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing…Kilian Lieret, Gage DeZoort, Devdoot Chatterjee et al.·Dec 6, 2023SaveLearn
Fast Posterior Probability Sampling with Normalizing Flows and Its Applicability in Bayesian analysis in Particle PhysicsIn this study, we use Rational-Quadratic Neural Spline Flows, a sophisticated parametrization of Normalizing Flows, for inferring posterior probability distributions in scenarios where direct…Mathias El Baz, Federico Sánchez·Dec 4, 2023SaveLearn