  We propose a new model independent technique for constructing background data
templates for use in searches for new physics processes at the LHC. This
method, called CURTAINs, uses invertible neural networks to parametrise the
distribution of side band data as a function of the resonant observable. The
network learns a transformation to map any data point from its value of the
resonant observable to another chosen value. Using CURTAINs, a template for the
background data in the signal window is constructed by mapping the data from
the side-bands into the signal region. We perform anomaly detection using the
CURTAINs background template to enhance the sensitivity to new physics in a
bump hunt. We demonstrate its performance in a sliding window search across a
wide range of mass values. Using the LHC Olympics dataset, we demonstrate that
CURTAINs matches the performance of other leading approaches which aim to
improve the sensitivity of bump hunts, can be trained on a much smaller range
of the invariant mass, and is fully data driven.
