skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

This content will become publicly available on April 9, 2020

Title: Imaging particle collision data for event classification using machine learning

Abstract

We propose a method to organize experimental data from particle collision experiments in a general format which can enable a simple visualisation and effective classification of collision data using machine learning techniques. The method is based on sparse fixed-size matrices with single- and two-particle variables containing information on identified particles and jets. As a result, we illustrate this method using an example of searches for new physics at the LHC experiments.

Authors:
 [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1508368
Report Number(s):
ANL-HEP-144006
Journal ID: ISSN 0168-9002; 144006
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Additional Journal Information:
Journal Volume: 931; Journal Issue: C; Journal ID: ISSN 0168-9002
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Classification; HEP LHC; Imaging; LHC; Machine learning; New physics; neural networks collisions

Citation Formats

Chekanov, Sergei V. Imaging particle collision data for event classification using machine learning. United States: N. p., 2019. Web. doi:10.1016/j.nima.2019.04.031.
Chekanov, Sergei V. Imaging particle collision data for event classification using machine learning. United States. doi:10.1016/j.nima.2019.04.031.
Chekanov, Sergei V. Tue . "Imaging particle collision data for event classification using machine learning". United States. doi:10.1016/j.nima.2019.04.031.
@article{osti_1508368,
title = {Imaging particle collision data for event classification using machine learning},
author = {Chekanov, Sergei V.},
abstractNote = {We propose a method to organize experimental data from particle collision experiments in a general format which can enable a simple visualisation and effective classification of collision data using machine learning techniques. The method is based on sparse fixed-size matrices with single- and two-particle variables containing information on identified particles and jets. As a result, we illustrate this method using an example of searches for new physics at the LHC experiments.},
doi = {10.1016/j.nima.2019.04.031},
journal = {Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment},
number = C,
volume = 931,
place = {United States},
year = {2019},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on April 9, 2020
Publisher's Version of Record

Save / Share: