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Thesis subjects
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DRF: Thesis subject SL-DRF-20-1004

RESEARCH FIELD
Particle physics / Corpuscular physics and outer space
TITLE English Français

Advanced artificial intelligence techniques for the event filtering at the CMS detector

ABSTRACT

After a very successful operation period crowned with the discovery of the Higgs boson, the Large Hadron Collider will undergo a luminosity upgrade where it is planned to increase the collision rate by a factor of ten. The CMS detector will also be upgraded to cope with these challenging environment and to enable a better event reconstruction, particularly with the new high-granularity calorimeters. Collecting, filtering and processing the data from these detectors will pose a significant challenge. In order to make the most out of it, the modern artificial intelligence techniques will be essential. We are looking for an enthusiastic student who will study the possible machine learning techniques that can be implemented in the Field Programable Gate Arrays (FPGA) to enable very high-speed reconstruction and filtering of this immense amount of data.

LOCATION
Institut de recherche sur les lois fondamentales de l’univers
Département d’Electronique, des Détecteurs et d’Informatique pour la physique
Systèmes Temps Réel, Electronique d’Acquisition et Microélectronique
Place: Saclay
Start date of the thesis: 01/10/2020
CONTACT PERSON

Mehmet Ozgur SAHIN  

CEA
DRF/IRFU/DEDIP/STREAM

Phone number: +33 1 69 08 14 67

UNIVERSITY / GRADUATE SCHOOL
Paris-Saclay
PHENIICS
THESIS SUPERVISOR

Fabrice COUDERC

CEA
DRF/IRFU/DPHP/CMS
CEA-Saclay Irfu/SPP