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Title: Boosting $$ H\to b\overline{b} $$ with machine learning

Abstract

High-p T Higgs production at hadron colliders provides a direct probe of the internal structure of the gg → H loop with the H→$$b\bar{b}$$ decay offering the most statistics due to the large branching ratio. Despite the overwhelming QCD background, recent advances in jet substructure have put the observation of the gg→H→$$b\bar{b}$$ channel at the LHC within the realm of possibility. In order to enhance the sensitivity to this process, we develop a two-stream convolutional neural network, with one stream acting on jet information and one using global event properties. The neural network significantly increases the discovery potential of a Higgs signal, both for high-p T Standard Model production as well for possible beyond the Standard Model contributions. Unlike most studies for boosted hadronically decaying massive particles, the boosted Higgs search is unique because double b-tagging rejects nearly all background processes that do not have two hard prongs. In this context — which goes beyond state-of-the-art two-prong tagging — the network is studied to identify the origin of the additional information leading to the increased significance. The procedures described here are also applicable to related final states where they can be used to identify additional sources of discrimination power that are not being exploited by current techniques.

Authors:
ORCiD logo [1];  [2];  [3];  [4]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
  2. Univ. of Oregon, Eugene, OR (United States)
  3. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1544187
Grant/Contract Number:  
AC02-05CH11231; SC0011640
Resource Type:
Accepted Manuscript
Journal Name:
Journal of High Energy Physics (Online)
Additional Journal Information:
Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2018; Journal Issue: 10; Journal ID: ISSN 1029-8479
Publisher:
Springer Berlin
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Jets

Citation Formats

Lin, Joshua, Freytsis, Marat, Moult, Ian, and Nachman, Benjamin. Boosting $ H\to b\overline{b} $ with machine learning. United States: N. p., 2018. Web. doi:10.1007/JHEP10(2018)101.
Lin, Joshua, Freytsis, Marat, Moult, Ian, & Nachman, Benjamin. Boosting $ H\to b\overline{b} $ with machine learning. United States. doi:10.1007/JHEP10(2018)101.
Lin, Joshua, Freytsis, Marat, Moult, Ian, and Nachman, Benjamin. Tue . "Boosting $ H\to b\overline{b} $ with machine learning". United States. doi:10.1007/JHEP10(2018)101. https://www.osti.gov/servlets/purl/1544187.
@article{osti_1544187,
title = {Boosting $ H\to b\overline{b} $ with machine learning},
author = {Lin, Joshua and Freytsis, Marat and Moult, Ian and Nachman, Benjamin},
abstractNote = {High-pT Higgs production at hadron colliders provides a direct probe of the internal structure of the gg → H loop with the H→$b\bar{b}$ decay offering the most statistics due to the large branching ratio. Despite the overwhelming QCD background, recent advances in jet substructure have put the observation of the gg→H→$b\bar{b}$ channel at the LHC within the realm of possibility. In order to enhance the sensitivity to this process, we develop a two-stream convolutional neural network, with one stream acting on jet information and one using global event properties. The neural network significantly increases the discovery potential of a Higgs signal, both for high-pT Standard Model production as well for possible beyond the Standard Model contributions. Unlike most studies for boosted hadronically decaying massive particles, the boosted Higgs search is unique because double b-tagging rejects nearly all background processes that do not have two hard prongs. In this context — which goes beyond state-of-the-art two-prong tagging — the network is studied to identify the origin of the additional information leading to the increased significance. The procedures described here are also applicable to related final states where they can be used to identify additional sources of discrimination power that are not being exploited by current techniques.},
doi = {10.1007/JHEP10(2018)101},
journal = {Journal of High Energy Physics (Online)},
number = 10,
volume = 2018,
place = {United States},
year = {2018},
month = {10}
}

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