Volume 414 - 41st International Conference on High Energy physics (ICHEP2022) - Poster Session
The boosted $X\rightarrow b\bar{b}$ tagger calibration using $Z\rightarrow b\bar{b}$ events collected with the ATLAS detector
D. Battulga
Full text: pdf
Pre-published on: November 05, 2022
Published on:
Abstract
Many analyses in the ATLAS physics program at the LHC 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. 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. The efficiency data to simulation scale factor is derived using the Run 2 $\textit{pp}$ collision data collected by the ATLAS experiment at $\sqrt{s}=13$ TeV, with the integrated luminosity of $139\,\text{fb}^{-1}$.
DOI: https://doi.org/10.22323/1.414.1089
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access