Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Mary Touranakou"'
Autor:
Breno Orzari, Nadezda Chernyavskaya, Raphael Cobe, Javier Duarte, Jefferson Fialho, Dimitrios Gunopulos, Raghav Kansal, Maurizio Pierini, Thiago Tomei, Mary Touranakou
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045023 (2023)
In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, becaus
Externí odkaz:
https://doaj.org/article/75b47c62b99d4ebaa1d38fe08ae5ec2f
Autor:
Mary Touranakou, Nadezda Chernyavskaya, Javier Duarte, Dimitrios Gunopulos, Raghav Kansal, Breno Orzari, Maurizio Pierini, Thiago Tomei, Jean-Roch Vlimant
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a919aeb17a9744473b24faf1696aefd
http://arxiv.org/abs/2203.00520
http://arxiv.org/abs/2203.00520
Autor:
Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Javier Duarte, Raghav Kansal, Jean-Roch Vlimant, Dimitrios Gunopulos
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of hadronic jets
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a58149a69b64a5aed8f71fc2655df311
http://cds.cern.ch/record/2784343
http://cds.cern.ch/record/2784343
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
Autor:
'Mary Touranakou
Publikováno v:
Nadezda Chernyavskaya