ENFPC 2019

Informações Gerais

• Datas: 01 a 05 de Setembro de 2019
• Local: Campos do Jordão, SP
• Hotel:
  • Nome a ser divulgado
  • Endereço
  • Telefone de Reserva

• Datas Limites:
  • Inscrições: 10/05
    • Data limite para os que desejam (doutores) apoio da FAPESP
    • Acréscimo de 25% após o prazo
  • Submissão de Resumos: 10/05
  • Pagamento de Inscrições: 31/07

• Transporte

Resumos

Instruções

• Data Limite:
• Entre 200 e 400 palavras
• Formato LateX

Posters

Ana

• Área: HEX - Física Experimental de Altas Energias
• Título:
• Autores: *Ana Maria Slivar, Eduardo Gregores
• Instituição: UFABC
• Resumo:
• Submetido/Aceito: Sim / XXX

Breno

• Área: HEX - Física Experimental de Altas Energias
• Título: *Breno XXX, Thiago Tomei
• Autores:
• Instituição:
• Resumo:

Dener

• Área: HEX - Física Experimental de Altas Energias
• Título: K0sK0s Bose-Einstein Correlations in pPb@8.16 TeV at CMS Detector
• Autores: *Dener Lemos, Sandra Padula
• Instituição: IFT-SPRACE-Unesp
• Resumo:

Eduardo

• Área: DCE - Divulgação e Ensino da Física Nuclear e de Partículas Elementares
• Título: SPRACE MasterClass na UFABC
• Autores: André Lessa, *Eduardo Gregores, Lucio Costa, Pedro Mercadante
• Instituição: UFABC
• Resumo:

Isabela

• Área: DCE - Divulgação e Ensino da Física Nuclear e de Partículas Elementares
• Título:
• Autores: *Isabela XXX, Sandra Padula
• Instituição:
• Resumo:

Tulio

• Área: HEX - Física Experimental de Altas Energias
• Título:
• Autores: Eduardo Gregores, *Tulio Cardoso
• Instituição: UFABC
• Resumo:

Apresentações Orais

Sandra

• Área: DCE - Divulgação e Ensino da Física Nuclear e de Partículas Elementares
• Título:
• Autores:
• Instituição:
• Resumo:

Thiago

• Área: HEX - Física Experimental de Altas Energias
• Título: Machine Learning Techniques for HL-LHC Tracking in CMS
• Autores: R. Cobe, J. Fialho, R. Iope, A. Santos, S. Stanzani, T. Tomei
• Instituição: SPRACE-Unesp
• Resumo: 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). At an average pileup of 140, in its standard configuration, the HL-LHC will deliver to CMS a data throughput of approximately 30 GB/s, doubling to 60 GB/s at the ultimate (PU = 200) configuration. Already on 2027, the CMS experiment estimates a need of 2.2 EB of disk, 3 EB of tape and 4.4M CPI cores, with only 200 to 300/fb of data collected. At the end of the full LHC + HL-LHC experimental run, the total collected luminosity will be on the order of 3000/fb. In order to deal with the increased amount of generated data and the complexity 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. We report on the exploration of the usage of advanced machine learning techniques for tracking at the HL-LHC, using the same dataset that was used for the TrackML Kaggle challenge.

Dener

• Área: PHE - Fenomenologia Hadrônica e de Partículas Elementares
• Título: CHESS: Complete Hydrodynamical Evolution SyStem
• Autores: *D. S. Lemos, O. Socolowski Jr.
• Instituição: IFT-SPRACE-Unesp and FURG
• Resumo: Under certain conditions of high energy density and temperature it is possible to observe the transition between the ordinary matter (made by hadrons) to quark-gluon plasma (QGP), where the quarks and gluons are not trapped. The QGP can be created in relativistic heavy-ion collisions, such as done at RHIC and LHC. One way to study such a complex system formed in the collisions is by using the hydrodynamic model. The application of this model is grounded in the hypothesis that this system will reach, rapidly, a local thermodynamic equilibrium state, and in the fact that system shows a collectivity behavior. Recently, experimental results have shown evidence of a similar behavior in small colliding systems (pp and pPb collisions) at high multiplicity. The Complete Hydrodynamic Evolution SyStem (CHESS) is a phenomenological package created to compare the results with the RHIC and LHC data. This code is designed to describe all the evolution of system formed in both heavy-ion collisions and small colliding systems using the hydrodynamical model (viscous and ideal) in 2+1 dimensions (boost invariance). The structure of the package is given by the three public codes connected by scripts in python language. With this code is possible to calculate many observables and compare with data, for example: rapidity distributions, invariant momentum distribution, flow and HBT effect.

-- gregores - 2019-04-25