Speaker
Description
Algorithms to identify jets from b-hadrons (b-jets) are widely in use in ATLAS and are crucial for measurements and searches targeting processes with top quarks or Higgs bosons in the final state, for instance, b-tagging played a central role in the observation of the Higgs boson decay into bottom quarks. These b-tagging algorithms are trained on simulation, therefore, their performance in data has to be checked and corrections for simulations have to be derived. Traditionally, operating points using intervals of fixed b-tagging efficiencies are defined such that calibrations can be performed in these intervals. However, this limits the flexibility in the application of the algorithms in analyses. This work introduces a continuous calibration of the ATLAS GN2 flavour-tagging algorithm using optimal transport maps. The simulated flavour-tagging classifier outputs (probabilities for b-, c-, tau-, and light-flavour jets) are transformed to match the observed data distributions, minimizing alterations to the simulation while achieving closure with data. The calibration is derived as a function of the jet transverse momentum and pseudorapidity using high-purity samples of b-jets from top-antitop events in ATLAS Run 3 data, employing neural solvers to approximate the optimal transport maps. This approach provides a fully continuous, three dimensional calibration of flavour probabilities, removing the need for discrete operating points and opening new possibilities for high-dimensional corrections in ATLAS analyses, therefore enhancing the precision of measurements and the sensitivity of searches for new physics.