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.
@article{arxiv.2311.17162,
title = {Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder},
author = {Ryan Liu and Abhijith Gandrakota and Jennifer Ngadiuba and Maria Spiropulu and Jean-Roch Vlimant},
journal= {arXiv preprint arXiv:2311.17162},
year = {2023}
}
Comments
7 pages, 4 figures, accepted at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023