2020/01/29 by Bora Işıldak, B. Isildak, Işıldak, Bora +5
Physics and Astronomy · #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Neutrino Physics Research #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an) #hep-ex #physics.data-an
paper · pdf · doi:10.48550/arxiv.2001.10814
6 pages, 7 figures, 5 tables
arxiv created 2020/01/29 · openalex publication_date 2020/01/29 · arxiv updated 2020/01/30 · openalex created_date 2020/02/07 · openalex updated_date 2026/07/28
A 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 one reconstructed variable to predict the value of unsmeared quantity on an event-by-event basis. In this particular study, deconvolution is interpreted as a classification problem, and neural networks (NN) are trained to deconvolute the Z boson invariant mass spectrum generated with MadGraph and pythia8 Monte Carlo event generators in order to prove the principle. Results obtained from the machine learning method is presented and compared with the results obtained with traditional methods.