  We propose exploiting symmetries (exact or approximate) of the Standard Model
(SM) to search for physics Beyond the Standard Model (BSM) using the
data-directed paradigm (DDP). Symmetries are very powerful because they provide
two samples that can be compared without requiring simulation. Focusing on the
data, exclusive selections which exhibit significant asymmetry can be
identified efficiently and marked for further study. Using a simple and generic
test statistic which compares two matrices already provides good sensitivity,
only slightly worse than that of the profile likelihood ratio test statistic
which relies on the exact knowledge of the signal shape. This can be exploited
for rapidly scanning large portions of the measured data, in an attempt to
identify regions of interest. Weakly supervised Neural Networks could be used
for this purpose as well.
