Autoencoders for Real-Time SUEP Detection
Simranjit Singh Chhibra, Nadezda Chernyavskaya, Benedikt Maier,, Maurzio Pierini, Syed Hasan

TL;DR
This paper presents a deep learning autoencoder approach for real-time detection of Soft Unclustered Energy Patterns (SUEP) at the LHC, aiming to identify dark sector signals amidst QCD backgrounds efficiently.
Contribution
The authors develop a novel autoencoder-based anomaly detection method that operates in real-time within the CMS High-Level Trigger system to identify SUEP events.
Findings
Detects 40% of SUEP events with 2% false positive rate.
Inference time of ~20 ms meets real-time system requirements.
Autoencoder trained on QCD data can generalize to new physics signatures.
Abstract
Confining dark sectors with pseudo-conformal dynamics can produce Soft Unclustered Energy Patterns (SUEP), at the Large Hadron Collider: the production of dark quarks in proton-proton collisions leading to a dark shower and the high-multiplicity production of dark hadrons. The final experimental signature is spherically-symmetric energy deposits by an anomalously large number of soft Standard Model particles with a transverse energy of O(100) MeV. Assuming Yukawa-like couplings of the scalar portal state, the dominant production mode is gluon fusion, and the dominant background comes from multi-jet QCD events. We have developed a deep learning-based Anomaly Detection technique to reject QCD jets and identify any anomalous signature, including SUEP, in real-time in the High-Level Trigger system of the Compact Muon Solenoid experiment at the Large Hadron Collider. A deep convolutional…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · Particle Detector Development and Performance
MethodsDice Loss
