Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Pratik Jawahar"'
Autor:
Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya, Maurizio Pierini, Kinga A. Wozniak, Jennifer Ngadiuba, Javier Duarte, Steven Tsan
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show
Externí odkaz:
https://doaj.org/article/bae1cafd2de14da4a85504bf90665db5
Autor:
Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya, Maurizio Pierini, Kinga A. Wozniak, Jennifer Ngadiuba, Javier Duarte, Steven Tsan
Publikováno v:
Frontiers in Big Data, 5
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bab4649403257edabb3b1e7db106c32a
https://pubmed.ncbi.nlm.nih.gov/35295683
https://pubmed.ncbi.nlm.nih.gov/35295683
Autor:
Thea Aarrestad, Melissa van Beekveld, Marcella Bona, Antonio Boveia, Sascha Caron, Joe Davies, Andrea de Simone, Caterina Doglioni, Javier Duarte, Amir Farbin, Honey Gupta, Luc Hendriks, Lukas A. Heinrich, James Howarth, Pratik Jawahar, Adil Jueid, Jessica Lastow, Adam Leinweber, Judita Mamuzic, Erzsébet Merényi, Alessandro Morandini, Polina Moskvitina, Clara Nellist, Jennifer Ngadiuba, Bryan Ostdiek, Maurizio Pierini, Baptiste Ravina, Roberto Ruiz de Austri, Sezen Sekmen, Mary Touranakou, Marija Vaškeviciute, Ricardo Vilalta, Jean-Roch Vlimant, Rob Verheyen, Martin White, Eric Wulff, Erik Wallin, Kinga A. Wozniak, Zhongyi Zhang
Publikováno v:
SciPost Physics, Vol 12, Iss 1, p 043 (2022)
SciPost physics 12(1), 043 (2022). doi:10.21468/SciPostPhys.12.1.043
SciPost physics 12(1), 043 (2022). doi:10.21468/SciPostPhys.12.1.043
We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2217453d53e1dc4ddff87b3c69bcc2b5
http://cds.cern.ch/record/2771263
http://cds.cern.ch/record/2771263
