  Classification of jets as originating from light-flavor or heavy-flavor
quarks is an important task for inferring the nature of particles produced in
high-energy collisions. The large and variable dimensionality of the data
provided by the tracking detectors makes this task difficult. The current
state-of-the-art tools require expert data-reduction to convert the data into a
fixed low-dimensional form that can be effectively managed by shallow
classifiers. We study the application of deep networks to this task, attempting
classification at several levels of data, starting from a raw list of tracks.
We find that the highest-level lowest-dimensionality expert information
sacrifices information needed for classification, that the performance of
current state-of-the-art taggers can be matched or slightly exceeded by
deep-network-based taggers using only track and vertex information, that
classification using only lowest-level highest-dimensionality tracking
information remains a difficult task for deep networks, and that adding
lower-level track and vertex information to the classifiers provides a
significant boost in performance compared to the state-of-the-art.
