  Strongly coupled hidden sector theories predict collider production of
invisible, composite dark matter candidates mixed with standard model hadrons
in the form of semivisible jets. Classical mass reconstruction techniques may
not be optimal for these unusual topologies, in which the missing transverse
momentum comes from massive particles and has a nontrivial relationship to the
visible jet momentum. We apply the artificial event variable network, a
semisupervised, interpretable machine learning technique that uses an
information bottleneck, to derive superior mass reconstruction functions for
several cases of resonant semivisible jet production. We demonstrate that the
technique can extrapolate to unknown signal model parameter values. We further
demonstrate the viability of conducting an actual search for new physics using
this method, by applying the learned functions to standard model background
events from quantum chromodynamics.
