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T: Fachverband Teilchenphysik

T 16: Data, AI, Computing 1 (anomaly detection)

T 16.2: Vortrag

Montag, 4. März 2024, 16:15–16:30, Geb. 30.33: MTI

VAE-based anomaly detection in dijet events at √s=13 TeV — •Aritra Bal¹, Benedikt Maier¹, Thea Aarrestad², Javier Duarte³, Markus Klute¹, Jennifer Ngadiuba⁴, Maurizio Pierini⁵, Kinga Wozniak⁵, and Irene Zoi⁴ — ¹Karlsruhe Institute of Technology — ²University of Zurich — ³University of California, San Diego — ⁴Fermi National Accelerator Laboratory — ⁵CERN

The reconstruction loss of an autoencoder can serve as a generic discriminator enabling anomaly searches. As part of the CMS Anomaly Search Effort (CASE), we present an approach for unsupervised anomaly detection in dijet events, by combining a variational autoencoder (VAE) with a novel technique for decorrelating the anomaly metric (i.e the autoencoder loss) from the dijet mass using a deep Quantile Regression. The resulting unsculpted spectra are then used to perform a bump hunt search that is sensitive to a range of narrow and broad signal resonances.

Keywords: Anomaly detection; Machine Learning; BSM physics