The boosted $X\rightarrow b\bar{b}$ tagger calibration using $Z\rightarrow b\bar{b}$ events collected with the ATLAS detector

Many analyses in the ATLAS physics program are dependent on the identification of jets containing b-hadrons (b-tagging). The corresponding algorithms are referred to as b-taggers. The baseline b-taggers are optimized for jets containing one b-hadron. A new double b-tagging algorithm, the $X\rightarrow b\bar{b}$ tagger, provides better identification efficiency to reconstruct boosted resonant particles decaying into a pair of b-quarks. In the boosted regime, it is a challenging task because of high collimation of the two b-hadrons. This neural network based $X\rightarrow b\bar{b}$ tagger uses the kinematic information of the large radius (R=1.0) jet and the flavour information of associated track-jets~\cite{ATL-PHYS-PUB-2020-019}. The performance of this tagger was evaluated using Monte Carlo simulation, therefore it could vary in collision data. Thus this poster presents the in situ tagging efficiency calibration using $Z\rightarrow b\bar{b}$ events with a recoiling photon or jet for this boosted $X\rightarrow b\bar{b}$ tagger~\cite{ATL-PHYS-PUB-2021-035}. The efficiency data to simulation scale factor is derived using the Run 2 \textit{pp} collision data collected by ATLAS experiment at $\sqrt{s}=13$ TeV, with the integrated luminosity of $139\,\text{fb}^{-1}$.