Autoencoders for Semivisible Jet Detection
| Title: | Autoencoders for Semivisible Jet Detection |
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| Authors: | Canelli, Florencia, de Cosa, Annapaola, Pottier, Luc Le, Niedziela, Jeremi, Pedro, Kevin, Pierini, Maurizio |
| Source: | Journal of High Energy Physics volume 2022, Article number: 74 (2022) |
| Publication Year: | 2021 |
| Collection: | Computer Science High Energy Physics - Experiment High Energy Physics - Phenomenology |
| Subject Terms: | High Energy Physics - Phenomenology, Computer Science - Machine Learning, High Energy Physics - Experiment |
| More Details: | The production of dark matter particles from confining dark sectors may lead to many novel experimental signatures. Depending on the details of the theory, dark quark production in proton-proton collisions could result in semivisible jets of particles: collimated sprays of dark hadrons of which only some are detectable by particle collider experiments. The experimental signature is characterised by the presence of reconstructed missing momentum collinear with the visible components of the jets. This complex topology is sensitive to detector inefficiencies and mis-reconstruction that generate artificial missing momentum. With this work, we propose a signal-agnostic strategy to reject ordinary jets and identify semivisible jets via anomaly detection techniques. A deep neural autoencoder network with jet substructure variables as input proves highly useful for analyzing anomalous jets. The study focuses on the semivisible jet signature; however, the technique can apply to any new physics model that predicts signatures with anomalous jets from non-SM particles. Comment: 17 pages, 10 figures |
| Document Type: | Working Paper |
| DOI: | 10.1007/JHEP02(2022)074 |
| Access URL: | http://arxiv.org/abs/2112.02864 |
| Accession Number: | edsarx.2112.02864 |
| Database: | arXiv |
| DOI: | 10.1007/JHEP02(2022)074 |
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