b-quark identification with the ATLAS detector at HL-LHC

Stage numéro : M2-2021-AT-01
Laboratoire :Centre de Physique des Particules de Marseille Case 902
 163 avenue de Luminy - 13288 Marseille Cedex 9
Directeur :Cristinel Diaconu - 04.91.82.72.01 - diaconu@cppm.in2p3.fr
Correspondant :William Gillard - 04.91.82.72.67 - gillard@cppm.in2p3.fr
Groupe d'accueil :Atlas
Chef de groupe :Marlon Barbero - 04.91.82.76.58 - barbero@cppm.in2p3.fr
Responsable de stage :Thomas Strebler - 04.91.82.72.52 - strebler@cppm.in2p3.fr

Thématique : Physique des particules

The Higgs boson was discovered in July 2012 by the ATLAS (http://atlas.cern) and CMS collaborations at the LHC, and led to a Nobel Prize for F. Englert and P. Higgs in 2013. Since then and till 2019, the LHC experiments have collected a lot of new data, in order to better characterize the Higgs boson and to possibly find evidences of new physics beyond the Standard Model.

However, in order to increase by a factor 100 the amount of useful data we already have, the LHC and its detectors will be upgraded for the High-Luminosity phase of LHC (2025-2035). The ATLAS group at CPPM (http://atlas.cppm.in2p3.fr), building on its previous expertise, is developing a new pixel detector to this end.

This high-tech detector plays a fundamental role to measure the trajectories of charged particles and to identify jets of particles stemming from the hadronization of bottom quarks. This ability, also known as b-tagging, is instrumental to the success of the ATLAS and (HL-)LHC physics program: Higgs coupling to top quarks and self-coupling, searches for new heavy particles.

The student will use detailed Monte-Carlo simulations to assess the b-tagging performance of various design options under consideration for the future pixel detector. The project provides an opportunity for the student to get an exposure to a broad spectrum of topics: LHC physics notably the Higgs sector; basics of silicon detectors, track-finding and pattern-recognition; b-tagging algorithms including Machine Learning algorithms (ANN, BDT, Deep Learning if time permits). The emphasis among those different topics will be chosen by the student. The project requires the use of the ROOT (http://root.cern.ch) analysis framework and the writing of C++ code: their prior knowledge is desirable but not mandatory.