Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

• URL: http://arxiv.org/abs/2311.17162v1
• Date: Tue, 28 Nov 2023 19:00:29 GMT
• Title: Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder
• Authors: Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba, Maria Spiropulu, Jean-Roch Vlimant
• Abstract summary: 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. With an eye to the future deployment to trigger systems, we propose the CLIP-VAE, which reduces the inference-time cost of anomaly detection.
• Score: 1.658130005539979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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