Real-Time Anomaly Detection in the CMS Level-1 Trigger with AXOL1TL

AXOL1TL is an anomaly detection (AD) trigger algorithm integrated into the Global Trigger (GT) of the CMS Level-1 Trigger (L1T) system since 2024. The GT reduces the event rate from proton–proton collisions at the LHC, lowering it from 40 MHz to 100 kHz within a fixed latency of 50 ns. The AD algorithm, implemented in the FPGA firmware of the GT board, uses an autoencoder to assign an anomaly score to each event, enabling the selection of more anomalous events for further analysis. We present the full deployment workflow to achieve ultra-low-latency anomaly detection: from hardware-aware model training to firmware synthesis and integration into the L1T system. We also report on the characterisation and performance of the AXOL1TL trigger, using the latest model, updated in 2025 with an increased rate budget and a novel feature extraction technique, based on a self-supervised method, which led to improved performance. This work demonstrates one of the first fully functional anomaly detection triggers within the CMS L1T system and showcases how novel trigger-level approaches can enhance sensitivity to new physics in real-time event selection.

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Publication Details

Published
2025-09-03
DOI
https://doi.org/10.17181/41mt0-3w586
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

Real-Time Anomaly Detection in the CMS Level-1 Trigger with AXOL1TL

Geochemistry and Geologic Mapping
article

Real-Time Anomaly Detection in the CMS Level-1 Trigger with AXOL1TL

article en

Abstract

AXOL1TL is an anomaly detection (AD) trigger algorithm integrated into the Global Trigger (GT) of the CMS Level-1 Trigger (L1T) system since 2024. The GT reduces the event rate from proton–proton collisions at the LHC, lowering it from 40 MHz to 100 kHz within a fixed latency of 50 ns. The AD algorithm, implemented in the FPGA firmware of the GT board, uses an autoencoder to assign an anomaly score to each event, enabling the selection of more anomalous events for further analysis. We present the full deployment workflow to achieve ultra-low-latency anomaly detection: from hardware-aware model training to firmware synthesis and integration into the L1T system. We also report on the characterisation and performance of the AXOL1TL trigger, using the latest model, updated in 2025 with an increased rate budget and a novel feature extraction technique, based on a self-supervised method, which led to improved performance. This work demonstrates one of the first fully functional anomaly detection triggers within the CMS L1T system and showcases how novel trigger-level approaches can enhance sensitivity to new physics in real-time event selection.

University of Padua (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Padova (IT)
Openalex Percentile: Top 42%
Geochemistry and Geologic Mapping
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