  We present the development and validation of the Higgs Optimized b
Identification Tagger (HOBIT), a multivariate b-jet identification algorithm
optimized for Higgs boson searches at the CDF experiment at the Fermilab
Tevatron. At collider experiments, b taggers allow one to distinguish particle
jets containing B hadrons from other jets; these algorithms have been used for
many years with great success at CDF. HOBIT has been designed specifically for
use in searches for light Higgs bosons decaying via H ! b\bar{b}. This fact
combined with the extent to which HOBIT synthesizes and extends the best ideas
of previous taggers makes HOBIT unique among CDF b-tagging algorithms.
Employing feed-forward neural network architectures, HOBIT provides an output
value ranging from approximately -1 ("light-jet like") to 1 ("b-jet like");
this continuous output value has been tuned to provide maximum sensitivity in
light Higgs boson search analyses. When tuned to the equivalent light jet
rejection rate, HOBIT tags 54% of b jets in simulated 120 GeV/c2 Higgs boson
events compared to 39% for SecVtx, the most commonly used b tagger at CDF. We
present features of the tagger as well as its characterization in the form of
b-jet finding efficiencies and false (light-jet) tag rates.
