Timing and characterization of shaped pulses with MHz ADCs in a detector system: a comparative study and deep learning approachTiming systems based on Analog-to-Digital Converters are widely used in the design of previous high energy physics detectors. In this paper, we propose a new method based on deep learning to extract…Pengcheng Ai, Dong Wang, Guangming Huang et al.·Jan 23, 2019SaveLearn
Local and Global Perspectives on Diffusion Maps in the Analysis of Molecular SystemsDiffusion maps approximate the generator of Langevin dynamics from simulation data. They afford a means of identifying the slowly-evolving principal modes of high-dimensional molecular systems. When…Zofia Trstanova, Ben Leimkuhler, Tony Lelièvre·Jan 21, 2019SaveLearn
Revising the stochastic iterative ensemble smootherEnsemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assimilation. Its…Patrick N. Raanes, Geir Evensen, Andreas S. Stordal·Jan 19, 2019SaveLearn
Direct ellipsoidal fitting of discrete multi-dimensional dataMulti-dimensional distributions of discrete data that resemble ellipsoids arise in numerous areas of science, statistics, and computational geometry. We describe a complete algebraic algorithm to…Rafey Anwar, Madeline Hamilton, Pavel Nadolsky·Jan 16, 2019SaveLearn
A binned likelihood for stochastic modelsMetrics of model goodness-of-fit, model comparison, and model parameter estimation are the main categories of statistical problems in science. Bayesian and frequentist methods that address these…Carlos A. Argüelles, Austin Schneider, Tianlu Yuan·Jan 15, 2019SaveLearn
Effects of hidden nodes on the reconstruction of bidirectional networksMuch research effort has been devoted to developing methods for reconstructing the links of a network from dynamics of its nodes. Many current methods require the measurements of the dynamics of all…Emily S. C. Ching, P. H. Tam·Jan 14, 2019SaveLearn
Data-driven inference of hidden nodes in networksThe explosion of activity in finding interactions in complex systems is driven by availability of copious observations of complex natural systems. However, such systems, e.g. the human brain, are…Danh-Tai Hoang, Junghyo Jo, Vipul Periwal·Jan 14, 2019SaveLearn
Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methodsImaging techniques are essential tools for inquiring a number of properties from different materials. Liquid crystals are often investigated via optical and image processing methods. In spite of…H. Y. D. Sigaki, R. F. de Souza, R. T. de Souza et al.·Jan 7, 2019SaveLearn
Symmetry-guided nonrigid registration: the case for distortion correction in multidimensional photoemission spectroscopyImage symmetrization is an effective strategy to correct symmetry distortion in experimental data for which symmetry is essential in the subsequent analysis. In the process, a coordinate transform,…Rui Patrick Xian, Laurenz Rettig, Ralph Ernstorfer·Jan 2, 2019SaveLearn
Efficiency correction for cumulants of multiplicity distributions based on track-by-track efficiencyWe propose a simplified procedure for the experimental application of the efficiency correction on higher order cumulants in heavy-ion collisions. By using the track-by-track efficiency, we can…Xiaofeng Luo, Toshihiro Nonaka·Dec 26, 2018SaveLearn
Event-shape engineering and heavy-flavour observables in relativistic heavy-ion collisionsTraditionally, events collected at relativistic heavy-ion colliders are classified according to some centrality estimator (e.g. the number of produced charged particles) related to the initial energy…A. Beraudo, A. De Pace, M. Monteno et al.·Dec 19, 2018SaveLearn
Bayesian parameter estimation of miss-specified modelsFitting a simplifying model with several parameters to real data of complex objects is a highly nontrivial task, but enables the possibility to get insights into the objects physics. Here, we present…Johannes Oberpriller, T. A. Enßlin·Dec 19, 2018SaveLearn
GooStats: A GPU-based framework for multi-variate analysis in particle physicsGooStats is a software framework that provides a flexible environment and common tools to implement multi-variate statistical analysis. The framework is built upon the CERN ROOT,…Xuefeng Ding·Dec 13, 2018SaveLearn
Probing high order dependencies with information theoryInformation theoretic measures (entropies, entropy rates, mutual information) are nowadays commonly used in statistical signal processing for real-world data analysis. The present work proposes the…C Granero-Belinchón, S. Roux, P. Abry et al.·Dec 13, 2018SaveLearn
Finding the origin of noise transients in LIGO data with machine learningQuality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics.…Marco Cavaglia, Kai Staats, Teerth Gill·Dec 13, 2018SaveLearn
Bias-Variance Trade-off and Model Selection for Proton Radius ExtractionsIntuitively, a scientist might assume that a more complex regression model will necessarily yield a better predictive model of experimental data. Herein, we disprove this notion in the context of…Douglas W. Higinbotham, Pablo Giuliani, Randall E. McClellan et al.·Dec 12, 2018SaveLearn
Establishing a common data base of ice experiments and using machine learning to understand and predict ice behaviorMachine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and small scale…Leon Kellner, Merten Stender, Hauke Herrnring et al.·Dec 11, 2018SaveLearn
Continuous cascades in the wavelet space as models for synthetic turbulenceWe introduce a wide family of stochastic processes that are obtained as sums of self-similar localized "waveforms" with multiplicative intensity in the spirit of the Richardson cascade…Jean-François Muzy·Dec 7, 2018SaveLearn
Generative Models for Fast Calorimeter Simulation.LHCb caseSimulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have…Viktoria Chekalina, Elena Orlova, Fedor Ratnikov et al.·Dec 4, 2018SaveLearn
Recycling cardiogenic artifacts in impedance pneumographyPurpose: Biomedical sensors often exhibit cardiogenic artifacts which, while distorting the signal of interest, carry useful hemodynamic information. We propose an algorithm to remove and extract…Yao Lu, Hau-tieng Wu, John Malik·Nov 27, 2018SaveLearn
Reconstruction of intermittent data time series as a superposition of pulsesFluctuations in a vast range of physical systems can be described as a superposition of uncorrelated pulses with a fixed shape, a process commonly referred to as a (generalized) shot noise or a…Sajidah Ahmed, Odd Erik Garcia, Audun Theodorsen·Nov 27, 2018SaveLearn
Extraction of Azimuthal Asymmetries using Optimal ObservablesAzimuthal asymmetries play an important role in scattering processes with polarized particles. This paper introduces a new procedure using event weighting to extract these asymmetries. It is shown…Jörg Pretz, Fabian Müller·Nov 23, 2018SaveLearn
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networksX-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to…Felipe Oviedo, Zekun Ren, Shijing Sun et al.·Nov 20, 2018SaveLearn
Learning from power system data stream: phasor-detective approachAssuming access to synchronized stream of Phasor Measurement Unit (PMU) data over a significant portion of a power system interconnect, say controlled by an Independent System Operator (ISO), what…Mauro Escobar, Daniel Bienstock, Michael Chertkov·Nov 17, 2018SaveLearn
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter InferenceThe unified approach of Feldman and Cousins allows for exact statistical inference of small signals that commonly arise in high energy physics. It has gained widespread use, for instance, in…Lingge Li, Nitish Nayak, Jianming Bian et al.·Nov 16, 2018SaveLearn