  Pixel detectors currently in use by high energy physics experiments such as
ATLAS, CMS, LHCb, etc., are critical systems for tagging $B$ hadrons within
particle jets. However, the performance of standard tagging algorithms begins
to fall in the case of highly boosted $B$ hadrons ($\gamma \beta = p/m >200$).
This paper builds on the work of our previous study that uses the jump in hit
multiplicity among the pixel layers when a $B$ hadron decays within the
detector volume. First, multiple $pp$ interactions within a finite luminous
region were found to have little effect. Second, the study has been extended to
use the multivariant techniques of an artificial neural network (ANN). After
training, the ANN shows significant improvements to the ability to reject
light-quark and charm jets; thus increasing the expected significance of the
technique.
