Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3

ATLAS Publications, J.F. Allen, T.M. Carter, D. Duda, J.M. Gargan, R.Y. Gonzalez Andana, A. Hasib, V.A. Parrish, E.A. Pender, T. Qiu, E.P. Takeva, N. Themistokleous, E.M. Villhauer, Z. Xu, E. Zaid

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The ATLAS experiment relies on real-time hadronic jet reconstruction and 𝑏-tagging to record fully hadronic events containing 𝑏-jets. These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has
mitigated these computational demands by introducing a fast neural-network-based 𝑏-tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model 𝐻𝐻 → 𝑏 ¯𝑏𝑏 ¯𝑏, a key signature relying on 𝑏-jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.
Original languageEnglish
Article numberP11006
Pages (from-to)1-36
Number of pages36
Journal Journal of Instrumentation
Issue number11
Publication statusPublished - 10 Nov 2023

Keywords / Materials (for Non-textual outputs)

  • Trigger algorithms
  • Trigger concepts and systems (hardware and software)


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