  We propose the first-ever complete, model-agnostic search strategy based on
the optimal anomaly score, for new physics on the tails of distributions.
Signal sensitivity is achieved via a classifier trained on auxiliary features
in a weakly-supervised fashion, and backgrounds are predicted using the ABCD
method in the classifier output and the primary tail feature. The independence
between the classifier output and the tail feature required for ABCD is
achieved by first training a conditional normalizing flow that yields a
decorrelated version of the auxiliary features; the classifier is then trained
on these features. Both the signal sensitivity and background prediction
require a sample of events accurately approximating the SM background; we
assume this can be furnished by closely related control processes in the data
or by accurate simulations, as is the case in countless conventional analyses.
The viability of our approach is demonstrated for signatures consisting of
(mono)jets and missing transverse energy, where the main SM background is
$Z(\nu \nu) +\text{jets}$, and the data-driven control process is
$\gamma+\text{jets}$.
