Implementation of Variational Autoencoder on the simulated particle collider data

Implementation of Variational Autoencoder on the simulated particle collider data

We study the possibility of applying deep learning algorithms, suchas Variational Autoencoders, on simulated particle collider data to detectBeyond the Standard Model events. In this report, we apply three dif-ferent processes of training the data for better eciency and the resultsof said training o...

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Bibliographic Details
Main Author: Alves Cardoso, Mário
Format: Others
Language:English
Published: Uppsala universitet, Högenergifysik 2021
Subjects:
high energy physics
machine learning
Physical Sciences
Fysik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-435325
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spelling ndltd-UPSALLA1-oai-DiVA.org-uu-4353252021-03-02T05:28:08ZImplementation of Variational Autoencoder on the simulated particle collider dataengAlves Cardoso, MárioUppsala universitet, Högenergifysik2021high energy physicsmachine learningPhysical SciencesFysikWe study the possibility of applying deep learning algorithms, suchas Variational Autoencoders, on simulated particle collider data to detectBeyond the Standard Model events. In this report, we apply three dif-ferent processes of training the data for better eciency and the resultsof said training on detecting anomalies. Links to the training and testingdata can be found here: https://www.phenomldata.org/ Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-435325FYSAST ; FYSPROJ1210application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic high energy physics
machine learning
Physical Sciences
Fysik
spellingShingle high energy physics
machine learning
Physical Sciences
Fysik
Alves Cardoso, Mário
Implementation of Variational Autoencoder on the simulated particle collider data
description We study the possibility of applying deep learning algorithms, suchas Variational Autoencoders, on simulated particle collider data to detectBeyond the Standard Model events. In this report, we apply three dif-ferent processes of training the data for better eciency and the resultsof said training on detecting anomalies. Links to the training and testingdata can be found here: https://www.phenomldata.org/
author Alves Cardoso, Mário
author_facet Alves Cardoso, Mário
author_sort Alves Cardoso, Mário
title Implementation of Variational Autoencoder on the simulated particle collider data
title_short Implementation of Variational Autoencoder on the simulated particle collider data
title_full Implementation of Variational Autoencoder on the simulated particle collider data
title_fullStr Implementation of Variational Autoencoder on the simulated particle collider data
title_full_unstemmed Implementation of Variational Autoencoder on the simulated particle collider data
title_sort implementation of variational autoencoder on the simulated particle collider data
publisher Uppsala universitet, Högenergifysik
publishDate 2021
url http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-435325
work_keys_str_mv AT alvescardosomario implementationofvariationalautoencoderonthesimulatedparticlecolliderdata
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