Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Touranakou, Mary"'
Autor:
Orzari, Breno, Chernyavskaya, Nadezda, Cobe, Raphael, Duarte, Javier, Fialho, Jefferson, Gunopulos, Dimitrios, Kansal, Raghav, Pierini, Maurizio, Tomei, Thiago, Touranakou, Mary
Publikováno v:
Mach. Learn.: Sci. Technol. 4 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:
http://arxiv.org/abs/2310.13138
Autor:
Touranakou, Mary, Chernyavskaya, Nadezda, Duarte, Javier, Gunopulos, Dimitrios, Kansal, Raghav, Orzari, Breno, Pierini, Maurizio, Tomei, Thiago, Vlimant, Jean-Roch
Publikováno v:
Mach. Learn.: Sci. Technol. 3, 035003 (2022)
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:
http://arxiv.org/abs/2203.00520
Autor:
Orzari, Breno, Tomei, Thiago, Pierini, Maurizio, Touranakou, Mary, Duarte, Javier, Kansal, Raghav, Vlimant, Jean-Roch, Gunopulos, Dimitrios
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:
http://arxiv.org/abs/2109.15197
Autor:
Kansal, Raghav, Duarte, Javier, Su, Hao, Orzari, Breno, Tomei, Thiago, Pierini, Maurizio, Touranakou, Mary, Vlimant, Jean-Roch, Gunopulos, Dimitrios
In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machine learning (ML)-based generative models, such as generative adversari
Externí odkaz:
http://arxiv.org/abs/2106.11535
Autor:
Kansal, Raghav, Duarte, Javier, Orzari, Breno, Tomei, Thiago, Pierini, Maurizio, Touranakou, Mary, Vlimant, Jean-Roch, Gunopulos, Dimitrios
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit i
Externí odkaz:
http://arxiv.org/abs/2012.00173
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