Machine learning techniques for jet reconstruction at LHCb and application to the search for $H \to b \bar{b}$ and $H \to c \bar{c}$ 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 $H \to \bbbar$ and $H \to c\barcc$ decays using a LHCb dataset corresponding to an integrated luminosity of 1.6\invfb. The observed (expected) 95\% confidence level upper limits correspond to 6.6 (11.1) times the SM cross-section for the $H \to b\bar b$ process, and 1003 (1834) times the SM cross-section for the $H \to c\bar c$ process.