End-to-end analysis using image classificationEnd-to-end analyses of data from high-energy physics experiments using machine and deep learning techniques have emerged in recent years. These analyses use deep learning algorithms to go directly…Adam Aurisano, Leigh H. Whitehead·Aug 5, 2022SaveLearn
Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering NoisePhysico-chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that…Yannick Kuhn, Hannes Wolf, Arnulf Latz et al.·Aug 4, 2022SaveLearn
Stochastic interpolation of sparsely sampled time series by a superstatistical random process and its synthesis in Fourier and wavelet spaceWe present a novel method for stochastic interpolation of sparsely sampled time signals based on a superstatistical random process generated from a multivariate Gaussian scale mixture. In comparison…Jeremiah Lübke, Jan Friedrich, Rainer Grauer·Aug 2, 2022SaveLearn
Unbiased two-windows approach for Welch's methodPeriodogram methods are widely used for the estimation of power- and cross-spectra, of which Welch's method is the most popular. Previous studies have analyzed the variance of the power spectra…Eduardo Martini·Jul 25, 2022SaveLearn
Measuring the attenuation length of muon number in the air shower with muon detectors of 3/4 LHAASO arrayLHAASO KM2A consists of 5915 scintillation detectors and 1188 muon detectors, and the muon detectors cover 4% area of the whole array with 30 m spacing. The muon number of air shower events, with…Xiaoting Feng, Hengying Zhang, Cunfeng Feng et al.·Jul 25, 2022SaveLearn
Machine Learned Particle Detector SimulationsThe use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a…D. Darulis, R. Tyson, D. G. Ireland et al.·Jul 22, 2022SaveLearn
Interpretable Boosted Decision Tree Analysis for the Majorana DemonstratorThe Majorana Demonstrator is a leading experiment searching for neutrinoless double-beta decay with high purity germanium detectors (HPGe). Machine learning provides a new way to maximize the amount…I. J. Arnquist, F. T. Avignone, A. S. Barabash et al.·Jul 21, 2022SaveLearn
Efficient Bayesian estimation of a non-Markovian Langevin model driven by correlated noiseData-driven modeling of non-Markovian dynamics is a recent topic of research with applications in many fields such as climate research, molecular dynamics, biophysics, or wind power modeling. In the…Clemens Willers, Oliver Kamps·Jul 21, 2022SaveLearn
Complexity emerges in measures of the marking dynamics in football gamesIn this article, we study the dynamics of marking in football matches. To do this, we surveyed and analyzed a database containing the trajectories of players from both teams on the field of play…A. Chacoma, M. N. Kuperman, O. V. Billoni·Jul 20, 2022SaveLearn
On the efficacy of the wisdom of crowds to forecast economic indicatorsThe interest in the wisdom of crowds stems mainly from the possibility of combining independent forecasts from experts in the hope that many expert minds are better than a few. Hence the relevant…Nilton S. Siqueira Neto, José F. Fontanari·Jul 18, 2022SaveLearn
Fast Columnar Physics Analyses of Terabyte-Scale LHC Data on a Cache-Aware Dask ClusterThe development of an LHC physics analysis involves numerous investigations that require the repeated processing of terabytes of data. Thus, a rapid completion of each of these analysis cycles is…Niclas Eich, Martin Erdmann, Peter Fackeldey et al.·Jul 18, 2022SaveLearn
A Software Tool for "Gluing" DistributionsWhen performing Monte-Carlo simulations, distributions are sometimes determined only for sub-intervals of the desired total range. In such cases, a frequent problem is to connect, or glue, individual…Peter Werner·Jul 18, 2022SaveLearn
Statistical significance testing for mixed priors: a combined Bayesian and frequentist analysisIn many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is…Jakob Robnik, Uroš Seljak·Jul 14, 2022SaveLearn
GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter ResponseHigh energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New…Alexander Rogachev, Fedor Ratnikov·Jul 13, 2022SaveLearn
Entropy estimation in bidimensional sequencesWe investigate the performance of entropy estimation methods, based either on block entropies or compression approaches, in the case of bidimensional sequences. We introduce a validation dataset made…F. N. M. de Sousa Filho, V. G. Pereira de Sá, E. Brigatti·Jul 6, 2022SaveLearn
Cover Your Bases: Asymptotic Distributions of the Profile Likelihood Ratio When Constraining Effective Field Theories in High-Energy PhysicsWe investigate the asymptotic distribution of the profile likelihood ratio (PLR) when constraining effective field theories (EFTs) and show that Wilks' theorem is often violated, meaning that we…Florian U. Bernlochner, Daniel C. Fry, Stephen B. Menary et al.·Jul 4, 2022SaveLearn
Low probability states, data statistics, and entropy estimationA fundamental problem in analysis of complex systems is getting a reliable estimate of entropy of their probability distributions over the state space. This is difficult because unsampled states can…Damián G. Hernández, Ahmed Roman, Ilya Nemenman·Jul 3, 2022SaveLearn
Constraining Model Uncertainty in Plasma Equation-of-State Models with a Physics-Constrained Gaussian ProcessEquation-of-state (EOS) models underpin numerical simulations at the core of research in high energy density physics, inertial confinement fusion, laboratory astrophysics, and elsewhere. In these…Jim A Gaffney, Lin Yang, Suzanne Ali·Jul 1, 2022SaveLearn
Confidence curves for UQ validation: probabilistic reference vs. oracleConfidence curves are used in uncertainty validation to assess how large uncertainties (uE) are associated with large errors (E). An oracle curve is commonly used as reference to estimate the…Pascal Pernot·Jun 30, 2022SaveLearn
Unsupervised real-world knowledge extraction via disentangled variational autoencoders for photon diagnosticsWe present real-world data processing on measured electron time-of-flight data via neural networks. Specifically, the use of disentangled variational autoencoders on data from a diagnostic instrument…Gregor Hartmann, Gesa Goetzke, Stefan Düsterer et al.·Jun 23, 2022SaveLearn
Markov property of the Super-MAG Auroral Electrojet IndicesThe dynamics of the Earth's magnetosphere exhibits strongly fluctuating patterns as well as non-stationary and non-linear interactions, more pronounced during magnetospheric substorms and magnetic…Simone Benella, Giuseppe Consolini, Mirko Stumpo et al.·Jun 21, 2022SaveLearn
Boosted decision treesBoosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the high-energy physics context and describing ways to quantify the…Yann Coadou·Jun 20, 2022SaveLearn
A machine learning photon detection algorithm for coherent X-ray ultrafast fluctuation analysisX-ray free electron laser (XFEL) experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One…Sathya R. Chitturi, Nicolas G. Burdet, Youssef Nashed et al.·Jun 18, 2022SaveLearn
Recurrence flow measure of nonlinear dependenceCouplings in complex real-world systems are often nonlinear and scale-dependent. In many cases, it is crucial to consider a multitude of interlinked variables and the strengths of their correlations…Tobias Braun, K. Hauke Kraemer, Norbert Marwan·Jun 10, 2022SaveLearn
Neural network model for imprecise regression with interval dependent variablesThis paper presents a computationally feasible method to compute rigorous bounds on the interval-generalisation of regression analysis to account for epistemic uncertainty in the output variables.…Krasymyr Tretiak, Georg Schollmeyer, Scott Ferson·Jun 6, 2022SaveLearn