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Interaction networks for the identification of boosted H→bb̅ decays

Moreno, Eric A. and Nguyen, Thong Q. and Vlimant, Jean-Roch and Cerri, Olmo and Newman, Harvey B. and Periwal, Avikar and Spiropulu, Maria and Duarte, Javier M. and Pierini, Maurizio (2020) Interaction networks for the identification of boosted H→bb̅ decays. Physical Review D, 102 (1). Art. No. 012010. ISSN 2470-0010. doi:10.1103/PhysRevD.102.012010.

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We develop a jet identification algorithm based on an interaction network, designed to identify high-momentum Higgs bosons decaying to bottom quark-antiquark pairs, distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated to them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of accurate LHC collisions, released by the CMS collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Moreno, Eric A.0000-0001-5666-3637
Nguyen, Thong Q.0000-0003-3954-5131
Vlimant, Jean-Roch0000-0002-9705-101X
Cerri, Olmo0000-0002-2191-0666
Newman, Harvey B.0000-0003-0964-1480
Spiropulu, Maria0000-0001-8172-7081
Duarte, Javier M.0000-0002-5076-7096
Pierini, Maurizio0000-0003-1939-4268
Additional Information:© 2020 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. Received 10 December 2019; accepted 13 June 2020; published 28 July 2020. This work was possible thanks to the commitment of the CMS Collaboration to release its simulation data through the CERN Open Data Portal. We would like to thank our CMS colleagues and the CERN Open Data team for their effort to promote open access to science. In particular, we thank Kati Lassila-Perini for her precious help. We are grateful to Caltech and the Kavli Foundation for their support of undergraduate student research in cross-cutting areas of machine learning and domain sciences. This work was conducted at “iBanks,” the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of iBanks. This project is partially supported by the United States Department of Energy, Office of High Energy Physics Research under Caltech Contract No. DE-SC0011925. M. P. is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 772369). J. M. D. is supported by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. E. A. M is supported by the Taylor W. Lawrence Research Fellowship and Mellon Mays Research Fellowship.
Funding AgencyGrant Number
Kavli FoundationUNSPECIFIED
Department of Energy (DOE)DE-SC0011925
European Research Council (ERC)772369
Department of Energy (DOE)DE-AC02-07CH11359
Taylor W. Lawrence Research FellowshipUNSPECIFIED
Mellon Mays Research FellowshipUNSPECIFIED
Issue or Number:1
Record Number:CaltechAUTHORS:20191022-101506524
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99386
Deposited By: Tony Diaz
Deposited On:22 Oct 2019 20:07
Last Modified:16 Nov 2021 17:46

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