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

Abstract

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 $HH\to b\bar{b}b\bar{b}$, 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%.

37 pages in total, author list starting page 20, 5 figures, 2 tables. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/TRIG-2022-03

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• The ATLAS Trigger System for LHC Run 3 and Trigger performance in 2022
• Neural networks for boosted di-$\tau$ identification
• Configuration, Performance, and Commissioning of the ATLAS $b$-jet Triggers for the 2022 and 2023 LHC data-taking periods
• B-jet Tagging Using a Hybrid Edge Convolution and Transformer Architecture
• Machine learning-based b-jet tagging in $pp$ collisions at $\sqrt{s}=13$ TeV
• Single Higgs boson production in association with a top quark through FCNSI