Enhancing sensitivities to long-lived particles with high granularity calorimeters at the LHC

Jia Liu, Zhen Liu, Lian Tao Wang, Xiao Ping Wang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

The search for long-lived particles (LLP) is an exciting physics opportunity in the upcoming runs of the Large Hadron Collider. In this paper, we focus on a new search strategy of using the High Granularity Calorimeter (HGCAL), part of the upgrade of the CMS detector, in such searches. In particular, we demonstrate that the high granularity of the calorimeter allows us to see “shower tracks” in the calorimeter, and can play a crucial role in identifying the signal and suppressing the background. We study the potential reach of the HGCAL using a signal model in which the Standard Model Higgs boson decays into a pair of LLPs, h → XX. After carefully estimating the Standard Model QCD and the misreconstructed fake-track backgrounds, we give the projected reach for both an existing vector boson fusion trigger and a novel displaced-track-based trigger. Our results show that the best reach for the Higgs decay branching ratio, BR(h → XX), in the vector boson fusion channel is about O(10−4) with lifetime cτX ∼ 0.1–1 meters, while for the gluon gluon fusion channel it is about O(10−5–10−6) for similar lifetimes. For longer lifetime cτX ∼ 103 meters, our search could probe BR(h → XX) down to a few ×10−4(10−2) in the gluon gluon fusion (vector boson fusion) channels, respectively. In comparison with these previous searches, our new search shows enhanced sensitivity in complementary regions of the LLP parameter space. We also comment on many improvements can be implemented to further improve our proposed search.

Original languageEnglish (US)
Article number66
JournalJournal of High Energy Physics
Volume2020
Issue number11
DOIs
StatePublished - Nov 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

Keywords

  • Beyond Standard Model
  • Higgs Physics

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