Autoencoders for semivisible jet detection
Abstract 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 w...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-02-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP02(2022)074 |
| _version_ | 1818977107057836032 |
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| author | Florencia Canelli Annapaola de Cosa Luc Le Pottier Jeremi Niedziela Kevin Pedro Maurizio Pierini |
| author_facet | Florencia Canelli Annapaola de Cosa Luc Le Pottier Jeremi Niedziela Kevin Pedro Maurizio Pierini |
| author_sort | Florencia Canelli |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-20T16:22:29Z |
| format | Article |
| id | doaj.art-785d3e934f6b47c599441adc6c78f19c |
| institution | Directory Open Access Journal |
| issn | 1029-8479 |
| language | English |
| last_indexed | 2024-12-20T16:22:29Z |
| publishDate | 2022-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of High Energy Physics |
| spelling | doaj.art-785d3e934f6b47c599441adc6c78f19c2022-12-21T19:33:34ZengSpringerOpenJournal of High Energy Physics1029-84792022-02-012022211710.1007/JHEP02(2022)074Autoencoders for semivisible jet detectionFlorencia Canelli0Annapaola de Cosa1Luc Le Pottier2Jeremi Niedziela3Kevin Pedro4Maurizio Pierini5Department of Physics, University of ZurichETH Zurich, Institute for Particle Physics and AstrophysicsUniversity of CaliforniaETH Zurich, Institute for Particle Physics and AstrophysicsFermi National Accelerator LaboratoryExperimental Physics Department, European Organization for Nuclear Research (CERN)Abstract 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.https://doi.org/10.1007/JHEP02(2022)074Jet substructureBeyond Standard ModelDark MatterHadron-Hadron ScatteringJets |
| spellingShingle | Florencia Canelli Annapaola de Cosa Luc Le Pottier Jeremi Niedziela Kevin Pedro Maurizio Pierini Autoencoders for semivisible jet detection Journal of High Energy Physics Jet substructure Beyond Standard Model Dark Matter Hadron-Hadron Scattering Jets |
| title | Autoencoders for semivisible jet detection |
| title_full | Autoencoders for semivisible jet detection |
| title_fullStr | Autoencoders for semivisible jet detection |
| title_full_unstemmed | Autoencoders for semivisible jet detection |
| title_short | Autoencoders for semivisible jet detection |
| title_sort | autoencoders for semivisible jet detection |
| topic | Jet substructure Beyond Standard Model Dark Matter Hadron-Hadron Scattering Jets |
| url | https://doi.org/10.1007/JHEP02(2022)074 |
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