Work

CMS: Anomaly Detection Triggers

Year: 2025

Type: article

Abstract: Anomaly detection triggers offer a model-agnostic approach to capturing a wide range of beyond the Standard Model (BSM) signatures, including those from long-lived particles (LLPs). This talk presents an overview of the two machine learning-based anomaly detection triggers deployed in the CMS Level-1 trigger in 2024: AXOL1TL and CICADA. I will discuss their design, implementation, and integration into the CMS trigger. The talk will also outline future directions for enhancing sensitivity to LLPs with such triggers by incorporating Level-1 tracking and precision timing information.

Source: CERN Document Server (European Organization for Nuclear Research)

DOI: 10.17181/pk97k-4ba06

Author: Pearkes, Jannicke Andree

Institution: University of Colorado Boulder

Language: English

Topic: Particle Detector Development and Performance

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Schmidt Family Foundation

OpenAlex ID: W7081655048

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