Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

Journal Article · Tue Nov 28 04:00:00 EST 2023 · TBD

OSTI ID:2282460

Liu, Ryan ^[1]; Gandrakota, Abhijith ^[2]; Ngadiuba, Jennifer ^[2]; Spiropulu, Maria ^[3]; Vlimant, Jean-Roch ^[3]

---

1. LBL, Berkeley
2. Fermilab
3. Caltech

Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a 2x signal efficiency gain compared with traditional subjettiness-based jet selection. Furthermore, with an eye to the future deployment to trigger systems, we propose the CLIP-VAE, which reduces the inference-time cost of anomaly detection by using the KL-divergence loss as the anomaly score, resulting in a 2x acceleration in latency and reducing the caching requirement.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
• DOE Contract Number: AC02-07CH11359
• OSTI ID: 2282460
• Report Number(s): FERMILAB-PUB-23-749-CMS; arXiv:2311.17162; oai:inspirehep.net:2727864
• Journal Information: TBD, Journal Name: TBD
• Country of Publication: United States
• Language: English

Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS