Bayesian Optimization for machine learning algorithms in the context of Higgs searches at the CMS experimentMachine Learning algorithms, such as Boosted Decisions Trees and Deep Neural Network, are widely used in High-Energy-Physics. The aim of this study is to apply Bayesian Optimization to tune the…Oriel Kiss·Nov 6, 2019SaveLearn
SCOUT: Signal Correction and Uncertainty Quantification Toolbox in MATLABThis manuscript describes the software package SCOUT, which analyzes, characterizes, and corrects one-dimensional signals. Specifically, it allows to check and correct for stationarity, detect…Richard Semaan, Vikas Yadav·Nov 6, 2019SaveLearn
Randomized Computer Vision Approaches for Pattern Recognition in Timepix and Timepix3 DetectorsTimepix and Timepix3 are hybrid pixel detectors (256× 256 pixels), capable of tracking ionizing particles as isolated clusters of pixels. To efficiently analyze such clusters at potentially…Petr Mánek, Benedikt Bergmann, Petr Burian et al.·Nov 6, 2019SaveLearn
Parameter uncertainties in weighted unbinned maximum likelihood fitsParameter estimation via unbinned maximum likelihood fits is central for many analyses performed in high energy physics. Unbinned maximum likelihood fits using event weights, for example to…Christoph Langenbruch·Nov 4, 2019SaveLearn
Unrecognized Sources of Uncertainties (USU) in Experimental Nuclear DataEvaluated nuclear data uncertainties are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed…R. Capote, S. Badikov, A. Carlson et al.·Nov 2, 2019SaveLearn
Estimating quantities conserved by virtue of scale invariance in timeseriesIn contrast to the symmetries of translation in space, rotation in space, and translation in time, the known laws of physics are not universally invariant under transformation of scale. However, the…Erik D. Fagerholm, W. M. C. Foulkes, Yasir Gallero-Salas et al.·Nov 2, 2019SaveLearn
Correction of IQ mismatch for a particle tracking radarFor a better understanding of granular flow problems such as silo blockage, avalanche triggering, mixing and segregation, it is essential to have a `microscopic' view of individual particles. In…Felix Rech, Kai Huang·Nov 2, 2019SaveLearn
A robust principal component analysis for outlier identification in messy microcalorimeter dataA principal component analysis (PCA) of clean microcalorimeter pulse records can be a first step beyond statistically optimal linear filtering of pulses towards a fully non-linear analysis. For PCA…J. W. Fowler, B. K. Alpert, Y. -I. Joe et al.·Nov 1, 2019SaveLearn
Comparison of unfolding methods using RooFitUnfoldIn this paper we describe RooFitUnfold, an extension of the RooFit statistical software package to treat unfolding problems, and which includes most of the unfolding methods that commonly used in…Lydia Brenner, Pim Verschuuren, Rahul Balasubramanian et al.·Oct 31, 2019SaveLearn
Template-free Pulse Height Estimation of Microcalorimeter Responses with PCAWe present a template-free method of estimating pulse height of micro-calorimeter signals based on principal component analysis (PCA). The method is shown to improve the resolution on a simulated…To Chin Yu·Oct 31, 2019SaveLearn
An efficient approach to global sensitivity analysis and parameter estimation for line gratingsScatterometry is a fast, indirect and nondestructive optical method for the quality control in the production of lithography masks. Geometry parameters of line gratings are obtained from diffracted…Nando Farchmin, Martin Hammerschmidt, Philipp-Immanuel Schneider et al.·Oct 31, 2019SaveLearn
Analytical representation of Gaussian processes in the A-T planeClosed-form expressions, parametrized by the Hurst exponent H and the length n of a time series, are derived for paths of fractional Brownian motion (fBm) and fractional Gaussian noise (fGn) in…Mariusz Tarnopolski·Oct 30, 2019SaveLearn
zfit: scalable pythonic fittingStatistical modeling is a key element in many scientific fields and especially in High-Energy Physics (HEP) analysis. The standard framework to perform this task in HEP is the C++ ROOT/RooFit…Jonas Eschle, Albert Puig Navarro, Rafael Silva Coutinho et al.·Oct 29, 2019SaveLearn
Nonlinear Correlations in Multifractals: Visibility Graphs of Magnitude and Sign SeriesCorrelations in multifractal series have been investigated, extensively. Almost all approaches try to find scaling features of a given time series. However, the analysis of such scaling properties…Pouya Manshour·Oct 29, 2019SaveLearn
Reconstruction of Current Densities from Magnetic Images by Bayesian InferenceElectronic transport is at the heart of many phenomena in condensed matter physics and material science. Magnetic imaging is a non-invasive tool for detecting electric current in materials and…Colin B. Clement, James P. Sethna, Katja C. Nowack·Oct 28, 2019SaveLearn
A 5D, polarised, Bethe-Heitler event generator for γ μ+μ- conversionI describe a five-dimensional, polarised, Bethe-Heitler event generator of γ-ray conversions to μ+μ-, based on a generator for conversion to e+e- developed in the past. Verifications are…Denis Bernard·Oct 28, 2019SaveLearn
Disentangling synchrony from serial dependency in paired event time seriesQuantifying synchronization phenomena based on the timing of events has recently attracted a great deal of interest in various disciplines such as neuroscience or climatology. A multitude of…Adrian Odenweller, Reik V. Donner·Oct 27, 2019SaveLearn
Extending RECAST for Truth-Level ReinterpretationsRECAST is an analysis reinterpretation framework; since analyses are often sensitive to a range of models, RECAST can be used to constrain the plethora of theoretical models without the significant…Alex Schuy, Lukas Heinrich, Kyle Cranmer et al.·Oct 23, 2019SaveLearn
Fragment Graphical Variational AutoEncoding for Screening Molecules with Small DataIn the majority of molecular optimization tasks, predictive machine learning (ML) models are limited due to the unavailability and cost of generating big experimental datasets on the specific task.…John Armitage, Leszek J. Spalek, Malgorzata Nguyen et al.·Oct 21, 2019SaveLearn
Sampling strategy and statistical analysis for radioactive waste characterizationThis paper describes the methodology we have developed to define a sampling strategy adapted to operational constraints in order to characterize the dihydrogen flow rate of 2714 nuclear waste drums…Nadia Perot, Alexandre Le Cocguen, Dominique Carré et al.·Oct 18, 2019SaveLearn
Identifying extra high frequency gravitational waves generated from oscillons with cuspy potentials using deep neural networksDuring oscillations of cosmology inflation around the minimum of a cuspy potential after inflation, the existence of extra high frequency gravitational waves (HFGWs) (GHz) has been proven effectively…Li Li Wang, Jin Li, Nan Yang et al.·Oct 17, 2019SaveLearn
Iterative procedure for network inferenceWhen a network is reconstructed from data, two types of errors can occur: false positive and false negative errors about the presence or absence of links. In this paper, the vertex degree…Gloria Cecchini, Bjoern Schelter·Oct 15, 2019SaveLearn
Parametrizing the Detector Response with Neural NetworksIn high energy physics, characterizing the response of a detector to radiation is one of the most important and basic experimental tasks. In many cases, this task is accomplished by parameterizing…Sanha Cheong, Aviv Cukierman, Benjamin Nachman et al.·Oct 9, 2019SaveLearn
Characterizing stochastic time series with ordinal networksApproaches for mapping time series to networks have become essential tools for dealing with the increasing challenges of characterizing data from complex systems. Among the different algorithms, the…Arthur A. B. Pessa, Haroldo V. Ribeiro·Oct 3, 2019SaveLearn
Generic predictions of output probability based on complexities of inputs and outputsFor a broad class of input-output maps, arguments based on the coding theorem from algorithmic information theory (AIT) predict that simple (low Kolmogorov complexity) outputs are exponentially more…Kamaludin Dingle, Guillermo Valle Pérez, Ard A. Louis·Oct 2, 2019SaveLearn