  The $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process is an
essential channel to reveal the Higgs properties but has an irreducible
background from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process,
which produces a top quark pair in association with a b quark pair. Therefore,
understanding the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process is
crucial for improving the sensitivity of a search for the
$\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process. To this end,
when measuring the differential cross-section of the
$\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, we need to distinguish
the b-jets originated from top quark decays, and additional b-jets originated
from gluon splitting. Since there are no simple identification rules, we adopt
deep learning methods to learn from data to identify the additional b-jets from
the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ events. Specifically, by
exploiting the special structure of the
$\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ event data, we propose several
loss functions that can be minimized to directly increase the matching
efficiency, the accuracy of identifying additional b-jets. We discuss the
difference between our method and another deep learning-based approach based on
binary classification arXiv:1910.14535 using synthetic data. We then verify
that additional b-jets can be identified more accurately by increasing matching
efficiency directly rather than the binary classification accuracy, using
simulated $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ event data in the
lepton+jets channel from pp collision at $\sqrt{s}$ = 13 TeV.
