| ATLAS Note | |
| Report number | ATL-DAQ-PROC-2025-020 |
| Title | GELATO: A Generic Event-Level Anomalous Trigger Option for ATLAS in LHC Run 3 |
| Author(s) | Sugizaki, Kaito (University of Pennsylvania (US)) ; ATLAS Collaboration |
| Corporate Author(s) | The ATLAS collaboration |
| Collaboration | ATLAS Collaboration |
| Publication | 2025 |
| Imprint | 30 Oct 2025 |
| Number of pages | 5 |
| In: | The 32nd International Symposium on Lepton Photon Interactions at High Energies (Lepton Photon 2025), Madison, Wisconsin, Us, 25 - 29 Aug 2025 |
| Subject category | Particle Physics - Experiment |
| Accelerator/Facility, Experiment | CERN LHC ; ATLAS |
| Free keywords | Anomaly detection, GELATO, Trigger, TDAQ, Machine learning |
| Abstract | The search for physics beyond the Standard Model has been a long-standing subject at the LHC. The absence of a discovery of such signatures indicates that new physics may elude standard triggers; conventional triggers at the ATLAS experiment are constructed by setting thresholds on variables such as the particle momentum, targeting event topologies exclusive to specific models. Anomaly detection, a form of unsupervised machine learning, enables searches for signatures which deviate from the Standard Model without relying on particular model assumptions. We present the first anomaly detection trigger at ATLAS, newly developed and integrated for data-taking in LHC Run 3. In addition to its design and expected performance, we discuss its commissioning, validation, and operational robustness, along with some observations based on the newly collected data. The first anomaly detection trigger in ATLAS marks a milestone for machine learning-based, next-generation triggers and model-agnostic searches for new physics. |
| Copyright/License | preprint: © 2025-2026 CERN (License: CC-BY-4.0) |
ჩანაწერი შექმნილია 2025-10-30, ბოლოს შესწორებულია 2026-05-12
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