Speaker
Description
Unsupervised machine learning algorithms are powerful tools for identifying potential new physics at the LHC, enabling the separation of standard model (SM) background events from anomalous signal events without relying on predefined signal hypotheses. Autoencoders (AEs) are frequently employed in such tasks, but their effectiveness is often hindered by the reconstruction of outliers. In this work, we present the Wasserstein Normalized Autoencoder (WNAE), an improved version of the normalized autoencoder (NAE), designed to mitigate these challenges. We apply the WNAE to a search for semivisible jets (SVJs) in the CMS experiment, demonstrating its enhanced ability to distinguish signal from background events. Our results highlight the potential of WNAE-based anomaly detection to improve sensitivity in LHC searches for beyond-the-SM physics.
| AI keywords | Autoencoders; anomaly detection |
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