  Complete anomaly detection strategies that are both signal sensitive and
compatible with background estimation have largely focused on resonant signals.
Non-resonant new physics scenarios are relatively under-explored and may arise
from off-shell effects or final states with significant missing energy. In this
paper, we extend a class of weakly supervised anomaly detection strategies
developed for resonant physics to the non-resonant case. Machine learning
models are trained to reweight, generate, or morph the background, extrapolated
from a control region. A classifier is then trained in a signal region to
distinguish the estimated background from the data. The new methods are
demonstrated using a semi-visible jet signature as a benchmark signal model,
and are shown to automatically identify the anomalous events without specifying
the signal ahead of time.
