are identified in data and used to estimate expected yields of these backgrounds in the signal region. Samples of events in regions that are disjoint from the signal region in tagging probability and dijet mass but which are enhanced in [+]{} and [+]{} production are used to provide data-driven constraints on the [+]{} and [+]{} backgrounds, respectively. Finally, a binned maximum likelihood fit is carried out simultaneously in the signal region and in the control regions for all channels.

Higgs boson reconstruction {#sec:HrecoResolved}
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The candidate is reconstructed as two distinct [small-$R$]{} jets. The identification of jets among those arising from other flavours of quarks or gluons is achieved with the Deep Combined Secondary Vertex (DeepCSV) algorithm [@Sirunyan:2017ezt]. This algorithm encodes a multiclassifier based on advanced ML techniques and provides three output weights $p(\cPqb)$, $p(\PQc)$, and $p(\cPqu{}\cPqd{}\cPqs{}\Pg)$ which can be interpreted as the probabilities for a given jet to have originated from a bottom quark, a charm quark, or a gluon or light-flavour quark, respectively. By combining the various DeepCSV outputs, it is possible to define two discriminators for tagging. The inputs to the DeepCSV algorithm are variables constructed from observables associated with the reconstructed primary and secondary vertices, tracks, 