Autoencoders for Semivisible Jet Detection

Bibliographic Details
Title:	Autoencoders for Semivisible Jet Detection
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

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Items	– Name: Title Label: Title Group: Ti Data: Autoencoders for Semivisible Jet Detection – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Canelli%2C+Florencia%22">Canelli, Florencia</searchLink><br /><searchLink fieldCode="AR" term="%22de+Cosa%2C+Annapaola%22">de Cosa, Annapaola</searchLink><br /><searchLink fieldCode="AR" term="%22Pottier%2C+Luc+Le%22">Pottier, Luc Le</searchLink><br /><searchLink fieldCode="AR" term="%22Niedziela%2C+Jeremi%22">Niedziela, Jeremi</searchLink><br /><searchLink fieldCode="AR" term="%22Pedro%2C+Kevin%22">Pedro, Kevin</searchLink><br /><searchLink fieldCode="AR" term="%22Pierini%2C+Maurizio%22">Pierini, Maurizio</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of High Energy Physics volume 2022, Article number: 74 (2022) – Name: DatePubCY Label: Publication Year Group: Date Data: 2021 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science<br />High Energy Physics - Experiment<br />High Energy Physics - Phenomenology – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22High+Energy+Physics+-+Phenomenology%22">High Energy Physics - Phenomenology</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Machine+Learning%22">Computer Science - Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%22High+Energy+Physics+-+Experiment%22">High Energy Physics - Experiment</searchLink> – Name: Abstract Label: Description Group: Ab Data: 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.<br />Comment: 17 pages, 10 figures – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: DOI Label: DOI Group: ID Data: 10.1007/JHEP02(2022)074 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2112.02864" linkWindow="_blank">http://arxiv.org/abs/2112.02864</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2112.02864
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RecordInfo	BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/JHEP02(2022)074 Subjects: – SubjectFull: High Energy Physics - Phenomenology Type: general – SubjectFull: Computer Science - Machine Learning Type: general – SubjectFull: High Energy Physics - Experiment Type: general Titles: – TitleFull: Autoencoders for Semivisible Jet Detection Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Canelli, Florencia – PersonEntity: Name: NameFull: de Cosa, Annapaola – PersonEntity: Name: NameFull: Pottier, Luc Le – PersonEntity: Name: NameFull: Niedziela, Jeremi – PersonEntity: Name: NameFull: Pedro, Kevin – PersonEntity: Name: NameFull: Pierini, Maurizio IsPartOfRelationships: – BibEntity: Dates: – D: 06 M: 12 Type: published Y: 2021
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