Generalized asymptotic formulae for estimating statistical significance in high energy physics analysesWithin the framework of likelihood-based statistical tests for high energy physics measurements, we derive generalized expressions for estimating the statistical significance of discovery using the…M. J. Basso·Feb 8, 2021SaveLearn
Learning to Isolate MuonsDistinguishing between prompt muons produced in heavy boson decay and muons produced in association with heavy-flavor jet production is an important task in analysis of collider physics data. We…Julian Collado, Kevin Bauer, Edmund Witkowski et al.·Feb 3, 2021SaveLearn
Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraintsExponential Random Graph Models (ERGMs) have gained increasing popularity over the years. Rooted into statistical physics, the ERGMs framework has been successfully employed for reconstructing…Nicolò Vallarano, Matteo Bruno, Emiliano Marchese et al.·Jan 29, 2021SaveLearn
Development of a Vertex Finding Algorithm using Recurrent Neural NetworkDeep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for…Kiichi Goto, Taikan Suehara, Tamaki Yoshioka et al.·Jan 28, 2021SaveLearn
Inference of stochastic time series with missing dataInferring dynamics from time series is an important objective in data analysis. In particular, it is challenging to infer stochastic dynamics given incomplete data. We propose an expectation…Sangwon Lee, Vipul Periwal, Junghyo Jo·Jan 28, 2021SaveLearn
AdaPT: Adaptable Particle Tracking for Spherical Microparticles in Lab on Chip SystemsDue to its rising importance in science and technology in recent years, particle tracking in videos presents itself as a tool for successfully acquiring new knowledge in the field of life sciences…Kristina Dingel, Rico Huhnstock, André Knie et al.·Jan 28, 2021SaveLearn
Discovering dependencies in complex physical systems using Neural NetworksIn todays age of data, discovering relationships between different variables is an interesting and a challenging problem. This problem becomes even more critical with regards to complex dynamical…Sachin Kasture·Jan 27, 2021SaveLearn
Pattern Ensembling for Spatial Trajectory ReconstructionDigital sensing provides an unprecedented opportunity to assess and understand mobility. However, incompleteness, missing information, possible inaccuracies, and temporal heterogeneity in the…Shivam Pathak, Mingyi He, Sergey Malinchik et al.·Jan 25, 2021SaveLearn
Enhancing the accuracy of a data-driven reconstruction of bivariate jump-diffusion models with corrections for higher orders of the sampling intervalWe evaluate the significance of a recently proposed bivariate jump-diffusion model for a data-driven characterization of interactions between complex dynamical systems. For various coupled and…Esra Aslim, Thorsten Rings, Lina Zabawa et al.·Jan 24, 2021SaveLearn
Interlaboratory consensus building challengeThe manuscript is about an interlaboratory comparison which involved eleven metrology institutes. It comprises four tasks: i) deriving a consensus value from these results; ii) evaluating the…Giovanni Mana·Jan 23, 2021SaveLearn
Reconstruction of nanoscale particles from single-shot wide-angle FEL diffractions patterns with physics-informed neural networksSingle-shot wide-angle diffraction imaging is a widely used method to investigate the structure of non-crystallizing objects such as nanoclusters, large proteins or even viruses. Its main advantage…Thomas Stielow, Stefan Scheel·Jan 22, 2021SaveLearn
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networksIn general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the…Joosep Pata, Javier Duarte, Jean-Roch Vlimant et al.·Jan 21, 2021SaveLearn
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopyDeep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of…Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck et al.·Jan 21, 2021SaveLearn
Detection of Dynamical Regime Transitions with Lacunarity as a Multiscale Recurrence Quantification MeasureWe propose lacunarity as a novel recurrence quantification measure and illustrate its efficacy to detect dynamical regime transitions which are exhibited by many complex real-world systems. We carry…Tobias Braun, Vishnu R. Unni, R. I. Sujith et al.·Jan 21, 2021SaveLearn
E Pluribus Unum Ex Machina: Learning from Many Collider Events at OnceThere have been a number of recent proposals to enhance the performance of machine learning strategies for collider physics by combining many distinct events into a single ensemble feature. To…Benjamin Nachman, Jesse Thaler·Jan 18, 2021SaveLearn
Improved Asymptotic Formulae for Statistical Interpretation Based on Likelihood Ratio TestsIn this work, we attempt to refine the classic asymptotic formulae to describe the probability distribution of likelihood-ratio statistical tests. The idea is to split the probability distribution…Li-Gang Xia, Yan Zhang·Jan 18, 2021SaveLearn
Fitting very flexible models: Linear regression with large numbers of parametersThere are many uses for linear fitting; the context here is interpolation and denoising of data, as when you have calibration data and you want to fit a smooth, flexible function to those data. Or…David W. Hogg, Soledad Villar·Jan 15, 2021SaveLearn
Convolutional neural network for self-mixing interferometric displacement sensingSelf mixing interferometry is a well established interferometric measurement technique. In spite of the robustness and simplicity of the concept, interpreting the self-mixing signal is often…Stéphane Barland, François Gustave·Jan 14, 2021SaveLearn
Network analysis with quantum dynamics clarifies why photosystem II exploits both chlorophyll a and bIn green plants, chlorophyll-a and chlorophyll-b are the predominant pigments bound to light-harvesting proteins. While the individual characteristics of these chlorophylls are well understood, the…Eunchul Kim, Daekyung Lee, Souichi Sakamoto et al.·Jan 13, 2021SaveLearn
Multivariate cumulants in flow analyses: The Next GenerationWe reconcile for the first time the strict mathematical formalism of multivariate cumulants with the usage of cumulants in anisotropic flow analyses in high-energy nuclear collisions. This…Ante Bilandzic, Marcel Lesch, Cindy Mordasini et al.·Jan 11, 2021SaveLearn
Neural Network for 3D ICF Shell Reconstruction from Single RadiographsIn inertial confinement fusion (ICF), X-ray radiography is a critical diagnostic for measuring implosion dynamics, which contains rich 3D information. Traditional methods for reconstructing 3D…Bradley T. Wolfe, Zhizhong Han, Jonathan S. Ben-Benjamin et al.·Jan 11, 2021SaveLearn
A Digital Quantum Algorithm for Jet Clustering in High-Energy PhysicsExperimental High-Energy Physics (HEP), especially the Large Hadron Collider (LHC) programme at the European Organization for Nuclear Research (CERN), is one of the most computationally intensive…Diogo Pires, Pedrame Bargassa, João Seixas et al.·Jan 11, 2021SaveLearn
Random Conical Tilt Reconstruction without Particle Picking in Cryo-electron MicroscopyWe propose a method to reconstruct the 3-D molecular structure from micrographs collected at just one sample tilt angle in the random conical tilt scheme in cryo-electron microscopy. Our method uses…Ti-Yen Lan, Nicolas Boumal, Amit Singer·Jan 10, 2021SaveLearn
Persistent Homology of Fractional Gaussian NoiseIn this paper, we employ the persistent homology (PH) technique to examine the topological properties of fractional Gaussian noise (fGn). We develop the weighted natural visibility graph algorithm,…H. Masoomy, B. Askari, M. N. Najafi et al.·Jan 9, 2021SaveLearn
Bayesian inference of 1D activity profiles from segmented gamma scanning of a heterogeneous radioactive waste drumWe present a Bayesian approach to probabilistically infer vertical activity profiles within a radioactive waste drum from segmented gamma scanning (SGS) measurements. Our approach resorts to Markov…Eric Laloy, Bart Rogiers, An Bielen et al.·Jan 6, 2021SaveLearn