A new Machine Learning-based method for identification of time-correlated events at tagged photon facilitiesWe present a new Machine Learning-based multivariate analysis method for the selection of time-correlated hits in the tagging system and devices used to detect particles in the final state at the…V. Sokhoyan, E. Mornacchi·Jul 9, 2023SaveLearn
Uncertainty components in profile likelihood fitsWhen a measurement of a physical quantity is reported, the total uncertainty is usually decomposed into statistical and systematic uncertainties. This decomposition is not only useful to understand…Andrés Pinto, Zhibo Wu, Fabrice Balli et al.·Jul 8, 2023SaveLearn
Linear approximation to the statistical significance autocovariance matrix in the asymptotic regimeApproximating significance scans of searches for new particles in high-energy physics experiments as Gaussian fields is a well-established way to estimate the trials factors required to quantify…V. Ananiev, A. L. Read·Jul 8, 2023SaveLearn
Projected Data Assimilation using Sliding Window Proper Orthogonal DecompositionPrediction of the state evolution of complex high-dimensional nonlinear systems is challenging due to the nonlinear sensitivity of the evolution to small inaccuracies in the model. Data Assimilation…Aishah Albarakati, Marko Budisic, Erik Van Vleck·Jul 6, 2023SaveLearn
Extended Dynamical Causal Modelling for Phase Coupling (eDCM PC)We present a software tool -- extended Dynamic Causal Modelling for Phase Coupling (eDCM PC) -- that is able to estimate effective connectivity between any kind of oscillating systems, e.g. distant…Azamat Yeldesbay, Silvia Daun·Jul 4, 2023SaveLearn
Reconstruction of Stochastic Dynamics from Large Streamed DatasetsThe complex dynamics of physical systems can often be modeled with stochastic differential equations. However, computational constraints inhibit the estimation of dynamics from large time-series…William Davis·Jul 2, 2023SaveLearn
The most likely common causeThe common cause principle for two random variables A and B is examined in the case of causal insufficiency, when their common cause C is known to exist, but only the joint probability of A…A. Hovhannisyan, A. E. Allahverdyan·Jun 30, 2023SaveLearn
Scattering Spectra Models for PhysicsPhysicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian…Sihao Cheng, Rudy Morel, Erwan Allys et al.·Jun 29, 2023SaveLearn
Learning thermodynamically constrained equations of state with uncertaintyNumerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density,…Himanshu Sharma, Jim A. Gaffney, Dimitrios Tsapetis et al.·Jun 29, 2023SaveLearn
Statistical analysis of event classification in experimental dataThe paper addresses general aspects of experimental data analysis, dealing with the separation of ``signal vs. background''. It consists of two parts. Part I is a tutorial on statistical event…Rudolf Frühwirth, Winfried Mitaroff·Jun 28, 2023SaveLearn
Regularization of the ensemble Kalman filter using a non-parametric, non-stationary spatial modelThe sample covariance matrix of a random vector is a good estimate of the true covariance matrix if the sample size is much larger than the length of the vector. In high-dimensional problems, this…Michael Tsyrulnikov, Arseniy Sotskiy·Jun 25, 2023SaveLearn
Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environmentsRecently, there has been significant interest in applying machine learning (ML) techniques to X-ray scattering experiments, which proves to be a valuable tool for enhancing research that involves…Linus Pithan, Vladimir Starostin, David Mareček et al.·Jun 20, 2023SaveLearn
Improving Estimation of the Koopman Operator with Kolmogorov-Smirnov Indicator FunctionsIt has become common to perform kinetic analysis using approximate Koopman operators that transforms high-dimensional time series of observables into ranked dynamical modes. Key to a practical…Van A. Ngo, Yen Ting Lin, Danny Perez·Jun 9, 2023SaveLearn
Forecast modeling a time series of water reservoir levels using exponential smoothing methodExponential smoothing is a time series forecasting method that presents the forecast based on trend and seasonality components. In this work, we study the behavior of two time series that describe…Lydiane F. Souza·Jun 8, 2023SaveLearn
An information field theory approach to Bayesian state and parameter estimation in dynamical systemsDynamical system state estimation and parameter calibration problems are ubiquitous across science and engineering. Bayesian approaches to the problem are the gold standard as they allow for the…Kairui Hao, Ilias Bilionis·Jun 3, 2023SaveLearn
Fast estimation of the look-elsewhere effect using Gaussian random fieldsWe discuss the use of Gaussian random fields to estimate the look-elsewhere effect correction. We show that Gaussian random fields can be used to model the null-hypothesis significance maps from a…Juehang Qin, Rafael F. Lang·Jun 2, 2023SaveLearn
Validating an algebraic approach to characterizing resonator networksResonator networks are ubiquitous in natural and engineered systems, such as solid-state materials, neural tissue, and electrical circuits. To understand and manipulate these networks, it is…Viva R. Horowitz, Brittany Carter, Uriel Hernandez et al.·Jun 2, 2023SaveLearn
PyPWA: A Software Toolkit for Parameter Optimization and Amplitude AnalysisPyPWA is a toolkit designed to optimize parametric models describing data and generate simulated distributions according to a model. Its software has been written within the python ecosystem with the…Mark Jones, Peter Hurck, William Phelps et al.·Jun 1, 2023SaveLearn
Singular Vectors of Sums of Rectangular Random Matrices and Optimal Estimators of High-Rank Signals: The Extensive Spike ModelAcross many disciplines from neuroscience and genomics to machine learning, atmospheric science and finance, the problems of denoising large data matrices to recover signals obscured by noise, and of…Itamar D. Landau, Gabriel C. Mel, Surya Ganguli·Jun 1, 2023SaveLearn
Transporting Densities Across DimensionsEven the best scientific equipment can only partially observe reality. Recorded data is often lower-dimensional, e.g., two-dimensional pictures of the three-dimensional world. Combining data from…Michael Plainer, Felix Dietrich, Ioannis G. Kevrekidis·May 25, 2023SaveLearn
Extension of the Langevin power curve analysis by separation per operational stateIn the last few years, the dynamical characterization of the power output of a wind turbine by means of a Langevin equation has been well established. For this approach, temporally highly resolved…Christian Wiedemann, Henrik M. Bette, Matthias Wächter et al.·May 24, 2023SaveLearn
Towards effective information content assessment: analytical derivation of information loss in the reconstruction of random fields with model uncertaintyStructures are abundant in both natural and human-made environments and usually studied in the form of images or scattering patterns. To characterize structures a huge variety of descriptors is…Aleksei Cherkasov, Kirill M. Gerke, Aleksey Khlyupin·May 23, 2023SaveLearn
Approach to Data Science with Multiscale Information TheoryData Science is a multidisciplinary field that plays a crucial role in extracting valuable insights and knowledge from large and intricate datasets. Within the realm of Data Science, two fundamental…Shahid Nawaz, Muhammad Saleem, F. V. Kusmartsev et al.·May 23, 2023SaveLearn
Review of the some specific features of the detecting of heavy recoilsIn this paper, we present the results of the first beam tests of the detection system at the focal plane of the Dubna Gas-Filled Recoil Separator-2 (DGFRS-2), which receives beams from the DC-280…Yu. S. Tsyganov, D. Ibadullayev, A. N. Polyakov et al.·May 22, 2023SaveLearn
Discovering Causal Relations and Equations from DataPhysics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena.…Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad et al.·May 21, 2023SaveLearn