CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum
  Likelihood Estimation
Debajyoti Sengupta Samuel Klein John Andrew Raine Tobias Golling
Model independent techniques for constructing background data templates using
generative models have shown great promise for use in searches for new physics
processes at the LHC. We introduce a major improvement to the CURTAINs method
by training the conditional normalizing flow between two side-band regions
using maximum likelihood estimation instead of an optimal transport loss. The
new training objective improves the robustness and fidelity of the transformed
data and is much faster and easier to train.
  We compare the performance against the previous approach and the current
state of the art using the LHC Olympics anomaly detection dataset, where we see
a significant improvement in sensitivity over the original CURTAINs method.
Furthermore, CURTAINsF4F requires substantially less computational resources to
cover a large number of signal regions than other fully data driven approaches.
When using an efficient configuration, an order of magnitude more models can be
trained in the same time required for ten signal regions, without a significant
drop in performance.