Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3
AU - ATLAS Collaboration
AU - Barton, A.E.
AU - Bertram, I.A.
AU - Borissov, G.
AU - Bouhova-Thacker, E.V.
AU - Ferguson, R.A.M.
AU - Fox, H.
AU - Henderson, R.C.W.
AU - Jones, R.W.L.
AU - Kartvelishvili, V.
AU - Love, P.A.
AU - Marshall, E.J.
AU - Meng, L.
AU - Muenstermann, D.
AU - Ribaric, N.
AU - Rybacki, K.
AU - Smizanska, M.
AU - Spinali, S.
AU - Wharton, A.M.
PY - 2023/11/10
Y1 - 2023/11/10
N2 - 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 → bb̅bb̅, 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%.
AB - 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 → bb̅bb̅, 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%.
U2 - 10.1088/1748-0221/18/11/P11006
DO - 10.1088/1748-0221/18/11/P11006
M3 - Journal article
VL - 18
JO - Journal of Instrumentation
JF - Journal of Instrumentation
SN - 1748-0221
IS - 11
M1 - P11006
ER -