Active Learning reinterpretation of an ATLAS Dark Matter search constraining a model of a dark Higgs boson decaying to two b-quarks

A reinterpretation of a search for dark matter produced in association with a Higgs boson decaying to $b$-quarks using active learning, a technique to facilitate efficient and comprehensive inference in multi-dimensional new physics parameter spaces, is presented. The dataset has an integrated luminosity of 139 fb$^{-1}$ and was recorded with the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}=$ 13 TeV. The reinterpretation refers to a model predicting dark matter production in association with a dark sector Higgs boson decaying to $b$-quarks. The active learning approach makes use of a Gaussian process to determine the exclusion limit contour and a corresponding uncertainty in a four-dimensional new physics parameter space. Each exclusion limit is determined accurately by means of the RECAST protocol. The combined approach of RECAST and active learning povides a blueprint for accurate, efficient and comprehensive interpretations of new physics searches at the Large Hadron Collider.