CERN Accelerating science

ATLAS Note
Report number	ATL-PHYS-PUB-2022-027
Title	Graph Neural Network Jet Flavour Tagging with the ATLAS Detector
Corporate Author(s)	The ATLAS collaboration
Publication	2022.
Collaboration	ATLAS Collaboration
Imprint	01 Jun 2022. - mult. p.
Note	All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2022-027
Subject category	Particle Physics - Experiment
Accelerator/Facility, Experiment	CERN LHC ; ATLAS
Free keywords	b-tagging ; c-tagging ; BTAGGING
Abstract	Flavour tagging, the identification of jets originating from $b$- and $c$-quarks, is a critical component of the physics programme of the ATLAS experiment at the Large Hadron Collider. Current flavour tagging algorithms rely on the outputs of several low-level algorithms, which reconstruct various properties of jets using charged particle tracks, that are then combined using machine learning techniques. In this note a new machine learning algorithm based on graph neural networks, GN1, is introduced. GN1 uses information from a variable number of charged particle tracks within a jet, to predict the jet flavour without the need for intermediate low-level algorithms. Alongside the jet flavour prediction, the model predicts which physics processes produced the different tracks in the jet, and groups tracks in the jet into vertices. These auxiliary training objectives provide useful addition information on the contents of the jet and improve performance. GN1 compares favourably with the current ATLAS flavour tagging algorithms. For a $b$-jet tagging efficiency of $70\%$ the light ($c$)-jet rejection is improved by a factor of  1.8 ( 2.1) for jets coming from $t\bar{t}$ decays with transverse momentum $20 < p_{T} < 250$ GeV. For jets coming from $Z'$ decays with transverse momentum $250 < p_{T} < 5000$ GeV the light ($c$)-jet rejection improves by a factor  6 ( 2.8) for a comparative $30\%$ $b$-jet efficiency.
Scientific contact person	Pamela Ferrari (pamela.ferrari@cern.ch)

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