Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3
- NázevTitle
- Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3
- Druh výsledkuResult type
- Článek v časopiseJournal article
- AutořiAuthors
- G. Aad, B. Abbott, K. Abeling, N. J. Abicht, B. Ali, K. Augsten, B. Bergmann, T. Billoud, H. Day-Hall, Z. Hubáček, P. Jačka, S. Mondal, M. Myška, L. Novotný, V. Petousis, R. Polifka, S. Pospíšil, K. Smolek, A. Sopczak, V. Vacek, P. Vokáč, O. Zaplatílek
- Klíčová slovaKeywords
- Trigger algorithms, Trigger concepts and systems (hardware and software)
- ExperimentCollaboration
- ATLAS
- DOIDOI
- 10.1088/1748-0221/18/11/P11006
- Časopis / citaceJournal / citation
- Journal of Instrumentation 18(11), P11006 (2023) · ISSN 1748-0221
- RokYear
- 2023
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- CERN-CZ III - Výzkumná infrastruktura pro experimenty v CERN - LM2023040 (2023–2026)CERN-CZ III - Výzkumná infrastruktura pro experimenty v CERN - LM2023040 (2023–2026); Institucionální podpora na rozvoj výzkumné org.Institucionální podpora na rozvoj výzkumné org.
- CitovánoCited by
- 42 (INSPIRE-HEP)
- Plný text (open access)Full text (open access)
- https://iopscience.iop.org/article/10.1088/1748-0221/18/11/P11006/pdf
- Citace ke staženíDownload citation
- TXT · BibTeX
AbstraktAbstract
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 (b) over barb (b) over bar, 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%.
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 (b) over barb (b) over bar, 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%.