Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principlesTransfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific…Kyle Sprague, Juan Carrasquilla, Steve Whitelam et al.·Mar 3, 2020SaveLearn
The statistical physics of discovering exogenous and endogenous factors in a chain of eventsEvent occurrence is not only subject to the environmental changes, but is also facilitated by the events that have occurred in a system. Here, we develop a method for estimating such extrinsic and…Shinsuke Koyama, Shigeru Shinomoto·Mar 2, 2020SaveLearn
Reconstructing particle number distributions with convoluting volume fluctuationsWe propose methods to reconstruct particle distributions with and without considering initial volume fluctuations. This approach enables us to correct for detector efficiencies and initial volume…ShinIchi Esumi, Kana Nakagawa, Toshihiro Nonaka·Feb 26, 2020SaveLearn
Automated detector simulation and reconstruction parametrization using machine learningRapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Currently available tools for the…D. Benjamin, S. V. Chekanov, W. Hopkins et al.·Feb 26, 2020SaveLearn
Modeling Aerial Gamma-Ray Backgrounds using Non-negative Matrix FactorizationAirborne gamma-ray surveys are useful for many applications, ranging from geology and mining to public health and nuclear security. In all these contexts, the ability to decompose a measured spectrum…M. S. Bandstra, T. H. Y. Joshi, K. J. Bilton et al.·Feb 24, 2020SaveLearn
Bryan's Maximum Entropy Method -- diagnosis of a flawed argument and its remedyThe Maximum Entropy Method (MEM) is a popular data analysis technique based on Bayesian inference, which has found various applications in the research literature. While the MEM itself is…Alexander Rothkopf·Feb 23, 2020SaveLearn
Using machine learning to separate hadronic and electromagnetic interactions in the GlueX forward calorimeterThe GlueX forward calorimeter is an array of 2800 lead glass modules that was constructed to detect photons produced in the decays of hadrons. A background to this process originates from hadronic…Rebecca Barsotti, Matthew R. Shepherd·Feb 21, 2020SaveLearn
Fast Time Series Detrending with Applications to Heart Rate Variability AnalysisHere we discuss a new fast detrending method for the non-stationary RR time series used in Heart Rate Variability analysis. The described method is based on the diffusion equation, and we show…M. Andrecut·Feb 16, 2020SaveLearn
Study of a data analysis method for the angle resolving silicon telescopeA new data analysis method is developed for the angle resolving silicon telescope introduced at the neutron time of flight facility nTOF at CERN. The telescope has already been used in measurements…P. Žugec, M. Barbagallo, J. Andrzejewski et al.·Feb 13, 2020SaveLearn
Uncertainty Quantification of Mode Shape Variation Utilizing Multi-Level Multi-Response Gaussian ProcessMode shape information play the essential role in deciding the spatial pattern of vibratory response of a structure. The uncertainty quantification of mode shape, i.e., predicting mode shape…Kai Zhou, Jiong Tang·Feb 12, 2020SaveLearn
Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image dataHigh-energy physics detectors, images, and point clouds share many similarities in terms of object detection. However, while detecting an unknown number of objects in an image is well established in…Jan Kieseler·Feb 10, 2020SaveLearn
On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use caseBeginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a…Giles Chatham Strong·Feb 3, 2020SaveLearn
Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network ApproachMuch research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these…Thanh Dung Le, Rita Noumeir, Huu Luong Quach et al.·Jan 29, 2020SaveLearn
Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic DataTwo dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently…Han Peng, Xiang Gao, Yu He et al.·Jan 29, 2020SaveLearn
Deconvolution of the High Energy Particle Physics Data with Machine LearningA method for correcting smearing effects using machine learning technique is presented. Compared to the standard deconvolution approaches in high energy particle physics, the method can use more than…Bora Işıldak, Alper Hayreter, Aidan R. Wiederhold·Jan 29, 2020SaveLearn
WISDoM: characterizing neurological timeseries with the Wishart distributionWISDoM (Wishart Distributed Matrices) is a new framework for the quantification of deviation of symmetric positive-definite matrices associated to experimental samples, like covariance or correlation…Carlo Mengucci, Daniel Remondini, Gastone Castellani et al.·Jan 28, 2020SaveLearn
BLUE: combining correlated estimates of physics observables within ROOT using the Best Linear Unbiased Estimate methodThis software performs the combination of m correlated estimates of n physics observables (m n) using the Best Linear Unbiased Estimate (BLUE) method. It is implemented as a C++ class, to be…Richard Nisius·Jan 28, 2020SaveLearn
Fast Algorithm for computing a matrix transform used to detect trends in noisy dataA recently discovered universal rank-based matrix method to extract trends from noisy time series is described in [1] but the formula for the output matrix elements, implemented there as an…D. J. Kestner, G. R. Ierley, A. B. Kostinski·Jan 27, 2020SaveLearn
A note on causation versus correlationRecently, it has been shown that the causality and information flow between two time series can be inferred in a rigorous and quantitative sense, and, besides, the resulting causality can be…X. San Liang, Xiuqun Yang·Jan 24, 2020SaveLearn
Variational Dropout Sparsification for Particle Identification speed-upAccurate particle identification (PID) is one of the most important aspects of the LHCb experiment. Modern machine learning techniques such as neural networks (NNs) are efficiently applied to this…Artem Ryzhikov, Denis Derkach, Mikhail Hushchyn·Jan 21, 2020SaveLearn
On a curious bias arising when the χ2/ν scaling prescription is first applied to a sub-sample of the individual resultsAs it is well known, the standard deviation of a weighted average depends only on the individual standard deviations, but not on the dispersion of the values around the mean. This property leads…Giulio D'Agostini·Jan 17, 2020SaveLearn
Correlations between Background Radiation inside a Multilayer Interleaving Structure, Geomagnetic Activity, and Cosmic Radiation: A Fourth Order Cumulant-based Correlation AnalysisIn this work, we analyzed time-series of background radiation inside a multilayer interleaving structure, geomagnetic activity and cosmic-ray activity using the Pearson correlation coefficient and a…M. E. Iglesias-Martínez, J. C. Castro-Palacio, F. Scholkmann et al.·Jan 16, 2020SaveLearn
Fully Bayesian Unfolding with RegularizationFully Bayesian Unfolding differs from other unfolding methods by providing the full posterior probability of unfolded spectra for each bin. We extended the method for the feature of regularization…Petr Baron·Jan 15, 2020SaveLearn
ABCNet: An attention-based method for particle taggingIn high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we…Vinicius Mikuni, Florencia Canelli·Jan 13, 2020SaveLearn
Measures of spike train synchrony and directionalityMeasures of spike train synchrony have become important tools in both experimental and theoretical neuroscience. Three time-resolved measures called the ISI-distance, the SPIKE-distance, and…Eero Satuvuori, Irene Malvestio, Thomas Kreuz·Jan 13, 2020SaveLearn