End-to-end jet classification of boosted top quarks with the CMS open data
Abstract
Here we describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique uses low-level detector representation of high-energy collision event as inputs to deep learning algorithms. In this study, we use low-level detector information from the simulated Compact Muon Solenoid (CMS) open data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves an area under the receiver operator curve (AUC) score of 0.975 ± 0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier AUC score increases to 0.9824 ± 0.0013, serving as the first performance benchmark for these CMS open data samples.
- Authors:
-
[1];
[2];
[4];
[1];
[2];
[3];
[2];
- Carnegie Mellon University, Pittsburgh, PA (United States)
- Brown University, Providence, RI (United States)
- Google Inc., Mountain View, CA (United States)
- University of Alabama, Tuscaloosa, AL (United States)
- Publication Date:
- Research Org.:
- Brown Univ., Providence, RI (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1980034
- Grant/Contract Number:
- SC0010010
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review. D.
- Additional Journal Information:
- Journal Volume: 105; Journal Issue: 5; Journal ID: ISSN 2470-0010
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Hadronic decays; quark and gluon jets; artificial neural networks; top quark
Citation Formats
Andrews, M., Burkle, B., Chen, Y., DiCroce, D., Gleyzer, S., Heintz, U., Narain, M., Paulini, M., Pervan, N., Shafi, Y., Sun, W., Usai, E., and Yang, K. End-to-end jet classification of boosted top quarks with the CMS open data. United States: N. p., 2022.
Web. doi:10.1103/physrevd.105.052008.
Andrews, M., Burkle, B., Chen, Y., DiCroce, D., Gleyzer, S., Heintz, U., Narain, M., Paulini, M., Pervan, N., Shafi, Y., Sun, W., Usai, E., & Yang, K. End-to-end jet classification of boosted top quarks with the CMS open data. United States. https://doi.org/10.1103/physrevd.105.052008
Andrews, M., Burkle, B., Chen, Y., DiCroce, D., Gleyzer, S., Heintz, U., Narain, M., Paulini, M., Pervan, N., Shafi, Y., Sun, W., Usai, E., and Yang, K. Tue .
"End-to-end jet classification of boosted top quarks with the CMS open data". United States. https://doi.org/10.1103/physrevd.105.052008. https://www.osti.gov/servlets/purl/1980034.
@article{osti_1980034,
title = {End-to-end jet classification of boosted top quarks with the CMS open data},
author = {Andrews, M. and Burkle, B. and Chen, Y. and DiCroce, D. and Gleyzer, S. and Heintz, U. and Narain, M. and Paulini, M. and Pervan, N. and Shafi, Y. and Sun, W. and Usai, E. and Yang, K.},
abstractNote = {Here we describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique uses low-level detector representation of high-energy collision event as inputs to deep learning algorithms. In this study, we use low-level detector information from the simulated Compact Muon Solenoid (CMS) open data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves an area under the receiver operator curve (AUC) score of 0.975 ± 0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier AUC score increases to 0.9824 ± 0.0013, serving as the first performance benchmark for these CMS open data samples.},
doi = {10.1103/physrevd.105.052008},
journal = {Physical Review. D.},
number = 5,
volume = 105,
place = {United States},
year = {Tue Mar 22 00:00:00 EDT 2022},
month = {Tue Mar 22 00:00:00 EDT 2022}
}
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