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 $H\to \text{bbbar}$ and $H\to c\text{barcc}$ 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.