Speaker
Description
The identification of jets containing b-hadrons, b-tagging, plays an important role in many physics analyses in ATLAS. Several different machine learning algorithms have been deployed for the purpose of b-tagging. These tagging algorithms are trained using Monte-Carlo simulation samples, as such their performance in data must be measured. The b-tagging efficiencies (epsilon_b) have been measured in data using ttbar events in the past and this work presents the measurements in multijet events using data collected by the ATLAS detector at sqrt{s}=13 TeV for the first time. This offers several key advantages over the ttbar based calibrations, including a higher precision at low jet pT and an ability to perform measurements of epsilon_b at significantly higher jet pT. Two approaches are applied and for both a profile likelihood fit is performed to extract the number of b-jets in samples passing and failing a given b-tagging requirement. The b-jets yields are then used to determine epsilon_b in data and from that scale factors to the efficiency measured in MC. The two approaches differ primarily in the discriminating variable used in the fit. At low jet pT the variable pT_rel is used, while for high jet pT the signed impact parameter significance is used. Both calibrations give measurements of the scale factors as a function of the jet pT.
| In-person participation | Yes |
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