{"id":{"repo_id":"radboud","oai_identifier":"oai:repository.ubn.ru.nl:2066/295258"},"canonical_url":"https://search.dev.ndltd.org/etd/radboud/oai:repository.ubn.ru.nl:2066/295258","repository":{"repo_id":"radboud","name":"Radboud University Nijmegen","base_url":"https://repository.ubn.ru.nl/oai/request"},"display":{"title":"Learning Physics as a Machine","abstract":"Contains fulltext : 295258.pdf (Publisher’s version ) (Open Access)","abstract_html":"Contains fulltext : 295258.pdf (Publisher’s version ) (Open Access)","abstract_has_math":false,"creators":["Otten, S.M.M."],"institution":"S.l. : s.n.","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Caron, S.","Bertone, G."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T04:01:40Z","subjects":["Experimental High Energy Physics","High Energy Physics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.ubn.ru.nl/handle/2066/295258","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Caron, S.","Bertone, G."]},{"key":"dc:creator","label":"Author","values":["Otten, S.M.M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023"]},{"key":"dc:publisher","label":"Institution","values":["S.l. : s.n."]},{"key":"dc:type","label":"Dc Type","values":["Doctoral thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Experimental High Energy Physics","High Energy Physics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://repository.ubn.ru.nl//bitstream/handle/2066/295258/295258.pdf","https://repository.ubn.ru.nl/handle/2066/295258"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Contains fulltext : 295258.pdf (Publisher’s version ) (Open Access)","This thesis provides several unique approaches to problems in physics leveraging machine learning techniques. After giving a brief introduction to the standard model and physics beyond, in particular supersymmetry, the ways to teach a machine applied within this thesis are explained. These fundamental chapters are followed by chapters based on five different publications: chapter 4 presents a neural network based tool to predict particle production cross-sections at the LHC for the pMSSM-19. Chapter 5 shows several applications, again for the prediction of cross-sections for BSM physics and a likelihood regressor for the MSSM-7. Chapter 6 explores applications of neural network and random forest based active learning to e.g. increase the resolution of the exclusion boundary for the pMSSM-19 and to identify uncertain regions for the steering of new searches. Chapter 7 presents a study on event generation with deep generative models, exploring several variants of Generative Adversarial Networks and Variational Autoencoders and attempts to learn the latent space to construct a probabilistic neural network model as an event generator. Chapter 8 applies neural networks and differentiable programming to strong gravitational lensing.","Radboud University, 06 september 2023","Promotores : Caron, S., Bertone, G.","XIX, 187 p."]},{"key":"dc:title","label":"Title","values":["Learning Physics as a Machine"]}]}],"canonical_facts":{"dc:contributor":["Caron, S.","Bertone, G."],"dc:creator":["Otten, S.M.M."],"dc:date":["2023"],"dc:description":["Contains fulltext : 295258.pdf (Publisher’s version ) (Open Access)","This thesis provides several unique approaches to problems in physics leveraging machine learning techniques. 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Chapter 7 presents a study on event generation with deep generative models, exploring several variants of Generative Adversarial Networks and Variational Autoencoders and attempts to learn the latent space to construct a probabilistic neural network model as an event generator. Chapter 8 applies neural networks and differentiable programming to strong gravitational lensing.","Radboud University, 06 september 2023","Promotores : Caron, S., Bertone, G.","XIX, 187 p."],"dc:identifier":["https://repository.ubn.ru.nl//bitstream/handle/2066/295258/295258.pdf","https://repository.ubn.ru.nl/handle/2066/295258"],"dc:publisher":["S.l. : s.n."],"dc:subject":["Experimental High Energy Physics","High Energy Physics"],"dc:title":["Learning Physics as a Machine"],"dc:type":["Doctoral thesis"]},"updated_at":"2026-07-24T04:01:40Z"}