Research Grants 22/02950-5 - Física experimental de altas energias, Física de alta energia - BV FAPESP
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Computing challenges for the CMS phase-II upgrade

Abstract

In this project, we present a High-Energy Physics research proposal with the CMS experiment of the Large Hadron Collider. The experiment has built and deployed a multipurpose detector capable of registering the proton-proton collisions delivered by the accelerator, with excellent particle reconstruction and identification capabilities and an acquisition rate of up to 1000 interactions per second. With the high-luminosity upgrade of the collider, which will bring the instantaneous luminosity to 75 Hz/nb, the CMS experiment will be revamped to make the most of the unprecedented amount of data that will be produced - L = 3000 fb-1 of pp collisions over the full experimental run. Those data will open up new avenues of research within CMS, including the accurate measurement of properties of the Higgs boson and new searches for physics beyond the standard model. On the other hand, the immense magnitude of this amount of data demands a paradigm shift for its treatment. In this project, we focus on two aspects of the computing challenges that must be overcome for the success of this endeavour. The first aspect is implementation of the High-Level Trigger of the experiment's data acquisition system, which will be updated to provide an event storage rate of 7.5 kHz, corresponding to a data transfer rate of 50 GB/s. The second aspect is the use of machine learning techniques in the experiment, which have been proving to be very powerful tools for handling datasets with many features and of great magnitude. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ORZARI, BRENO; CHERNYAVSKAYA, NADEZDA; COBE, RAPHAEL; DUARTE, JAVIER; FIALHO, JEFFERSON; GUNOPULOS, DIMITRIOS; KANSAL, RAGHAV; PIERINI, MAURIZIO; TOMEI, THIAGO; TOURANAKOU, MARY. LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows. MACHINE LEARNING-SCIENCE AND TECHNOLOGY, v. 4, n. 4, p. 12-pg., . (20/06600-3, 19/16401-0, 22/02950-5, 18/25225-9)

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