- Physical Review Journals
All JournalsPhysics Magazine
Access Provided by Xinjiang University

Physical Review D

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access
  • Access by Xinjiang University

Learning to isolate muons in data

Edmund Witkowski1,*, Benjamin Nachman2,3,†, and Daniel Whiteson1,‡

  • 1Department of Physics and Astronomy, University of California, Irvine, California 92697, USA
  • 2Lawrence Berkeley National Laboratory, Physics Division, Berkeley, California 94720, USA
  • 3Berkeley Institute for Data Science, University of California, Berkeley, California 94720, USA

  • *witkowse@uci.edu
  • †bpnachman@lbl.gov
  • ‡daniel@uci.edu
PDF

Phys. Rev. D 108, 092008 – Published 17 November, 2023

DOI: https://doi.org/10.1103/PhysRevD.108.092008

Abstract

We use unlabeled collision data and weakly supervised learning to train models that can distinguish prompt muons from nonprompt muons using patterns of low-level particle activity in the vicinity of the muon and interpret the models in the space of energy flow polynomials. Particle activity associated with muons is a valuable tool for identifying prompt muons, those due to heavy boson decay, from muons produced in the decay of heavy flavor jets. The high-dimensional information is typically reduced to a single scalar quantity, isolation, but previous work in simulated samples suggests that valuable discriminating information is lost in this reduction. We extend these studies in LHC collisions recorded by the CMS experiment, where true class labels are not available, requiring the use of the invariant mass spectrum to obtain macroscopic sample information. This allows us to employ classification without labels, a weakly supervised learning technique, to train models. Our results confirm that isolation does not describe events as well as the full low-level calorimeter information, and we are able to identify single energy flow polynomials capable of closing the performance gap. These polynomials are not the same ones derived from simulation, highlighting the importance of training directly on data.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (37)

  1. M. Aaboud et al. (ATLAS Collaboration), Phys. Rev. D 97, 052010 (2018).
  2. R. Schoefbeck, Nucl. Part. Phys. Proc. 273–275, 631 (2016).
  3. V. Khachatryan et al. (CMS Collaboration), J. High Energy Phys. 11 (2015) 189.
  4. ATLAS Collaboration, arXiv:2209.13935.
  5. A. Tumasyan et al. (CMS Collaboration), J. High Energy Phys. 04 (2022) 091.
  6. G. Aad et al. (ATLAS Collaboration), Phys. Rev. D 101, 052005 (2020).
  7. I. Hoenig, G. Samach, and D. Tucker-Smith, Phys. Rev. D 90, 075016 (2014).
  8. ATLAS Collaboration, arXiv:2301.09342.
  9. R. Aaij et al. (LHCb Collaboration), J. High Energy Phys. 10 (2020) 156.
  10. A. Tumasyan et al. (CMS Collaboration), Eur. Phys. J. C 82, 290 (2022).
  11. A. M. Sirunyan et al. (CMS Collaboration), J. Instrum. 12, P10003 (2017).
  12. G. Aad et al. (ATLAS Collaboration), Eur. Phys. J. C 81, 578 (2021).
  13. P. Baldi, P. Sadowski, and D. Whiteson, Nat. Commun. 5, 4308 (2014).
  14. L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, J. High Energy Phys. 07 (2016) 069.
  15. M. Feickert and B. Nachman, arXiv:2102.02770.
  16. J. Collado, K. Bauer, E. Witkowski, T. Faucett, D. Whiteson, and P. Baldi, J. High Energy Phys. 10 (2020) 200.
  17. G. Aad et al. (ATLAS Collaboration), Eur. Phys. J. C 76, 292 (2016).
  18. A. M. Sirunyan et al. (CMS Collaboration), J. Instrum. 13, P06015 (2018).
  19. C. Cesarotti, Y. Soreq, M. J. Strassler, J. Thaler, and W. Xue, Phys. Rev. D 100, 015021 (2019).
  20. E. M. Metodiev, B. Nachman, and J. Thaler, J. High Energy Phys. 10 (2017) 174.
  21. A. M. Sirunyan et al. (CMS Collaboration), Phys. Lett. B 803, 135285 (2020).
  22. V. M. Mikuni, Collider physics measurements in high jet multiplicity final states (2021), https://cds.cern.ch/record/2781479.
  23. J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. D 99, 014038 (2019).
  24. J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. Lett. 121, 241803 (2018).
  25. G. Aad et al. (ATLAS Collaboration), Phys. Rev. Lett. 125, 131801 (2020).
  26. P. T. Komiske, E. M. Metodiev, and J. Thaler, J. High Energy Phys. 04 (2018) 013.
  27. T. Faucett, J. Thaler, and D. Whiteson, Phys. Rev. D 103, 036020 (2021).
  28. S. Chatrchyan et al. (CMS Collaboration), J. Instrum. 3, S08004 (2008).
  29. CERN Open Data Portal, http://opendata.cern.ch.
  30. R. L. Workman et al. (Particle Data Group), Prog. Theor. Exp. Phys. 2022, 083C01 (2022).
  31. P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright et al., Nat. Methods 17, 261 (2020).
  32. M. Pivk and F. R. Le Diberder, Nucl. Instrum. Methods Phys. Res., Sect. A 555, 356 (2005).
  33. B. Nachman, SciPost Phys. 8, 090 (2020).
  34. B. Efron, Ann. Stat. 7, 1 (1979).
  35. M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov, and A. Smola, arXiv:1703.06114.
  36. P. T. Komiske, E. M. Metodiev, and J. Thaler, J. High Energy Phys. 01 (2019) 121.
  37. https://github.com/Edwit4/learning_to_isolate_muons_in_data.

Outline

Information

Phys. Rev. D 108, 092008– Published 17 November, 2023
  • Received 3 August 2023
  • Accepted 20 October 2023
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation