  Tagging jets of strongly interacting particles initiated by energetic strange
quarks is one of the few largely unexplored Standard Model object
classification problems remaining in high energy collider physics. In this
paper we investigate the purest version of this classification problem in the
form of distinguishing strange-quark jets from down-quark jets. Our strategy
relies on the fact that a strange-quark jet contains on average a higher ratio
of neutral kaon energy to neutral pion energy than does a down-quark jet.
Long-lived neutral kaons deposit energy mainly in the hadronic calorimeter of a
high energy detector, while neutral pions decay promptly to photons that
deposit energy mainly in the electromagnetic calorimeter. In addition,
short-lived neutral kaons that decay in flight to charged pion pairs can be
identified as a secondary vertex in the inner tracking system. Using these
handles we study different approaches to distinguishing strange-quark from
down-quark jets, including single variable cut-based methods, a boosted
decision tree (BDT) with a small number of simple variables, and a deep
learning convolutional neural network (CNN) architecture with jet images. We
show that modest gains are possible from the CNN compared with the BDT or a
single variable. Starting from jet samples with only strange-quark and
down-quark jets, the CNN algorithm can improve the strange to down ratio by a
factor of roughly 2 for strange tagging efficiencies below 0.2, and by a factor
of 2.5 for strange tagging efficiencies near 0.02.
