High Energy Physics (HEP) explores the elementary particles, which are the fundamental constituents of matter. HEP experiments led to important discoveries, such as the detection of the Higgs boson at the CERN Large Hadron Collider - LHC. The High-Luminosity LHC (HL-LHC) is the next challenge in the HEP scenario, bringing the collider’s instantaneous luminosity to 75 Hz/nb and increasing in 5 times the amount of additional pp interactions in the same or neighboring bunch crossings, referred to as pileup (PU). In order to deal with the increased amount of generated data and the complex- ity of the simulations, new techniques and frameworks have to be deployed and/or developed. In that scenario, the Deep Neural Networks (DNN) revolution can make a significant impact on HEP. These techniques are most promising when there are both a large amount of data and a high number of features. This project proposes an exploration of the usage of advanced machine learning techniques at the HL-LHC.
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Fernanda Magalhães
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CNPJ: 20.320.503/0001-51
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36570.900 - Viçosa/MG
Documentos úteis:
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2019-_Manual_coordenadores_Serrapilheira_15maio.pdf | r1 | manage | 439.9 K | 2019-05-22 - 20:34 | UnknownUser | Manual de Coordenadores do Serrapilheira |
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ThiagoTomei_ProjetoPesquisa.pdf | r1 | manage | 2189.5 K | 2019-05-03 - 16:29 | UnknownUser | Projeto submetido |
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