Machine learning techniques for jet reconstruction at LHCb and application to the search for $ H\to b\overline{b} $ and $ H\to c\overline{c} $ in $ \sqrt{s}=13 $ TeV pp collisions

Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between b -quark, c -quark, and light parton jets. These techniques are applied to a search for inclusive $H\to b\overline{b}$ and $H\to c\overline{c}$ decays using a LHCb dataset corresponding to an integrated luminosity of 1.6 fb − 1 . The observed (expected) 95% confidence level upper limits correspond to 6.6 (11.1) times the SM cross-section for the $H\to b\overline{b}$ process, and 1003 (1834) times the SM cross-section for the $H\to c\overline{c}$ process.