mass of the jets and lepton, compared to the unsmeared background and the signal samples.[]{data-label="fig:observables"}](figures/MET_plot.pdf "fig:"){width="\textwidth"}

[0.49]{} ![Effect of smearing on (a) the $p_T$ of the hardest $b$-jet, (b) the $p_T$ of the hardest light jet, (c) the missing energy and (d) the invariant mass of the jets and lepton, compared to the unsmeared background and the signal samples.[]{data-label="fig:observables"}](figures/minv_plot.pdf "fig:"){width="\textwidth"}

In Fig. \[fig:observables\] we show the effect of smearing on the $p_T$ of the hardest $b$-jet and light jet, the missing energy in the event and the invariant mass of the jets and lepton, compared to the equivalent distributions for the signal events. Clearly the smearing of the background has the potential to make it either easier or harder for a classifier to discriminate between signal and background, depending on which direction the smearing shifts the background distribution.

Decorrelated jet smearing with supervised adversarial classifier {#sec:supervised}
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To set a benchmark for the signal-to-background separation, we first train a simple neural network classifier to discriminate signal events from the complete set of background events (including all three samples). We expect this supervised-learning approach to perform better than the unsupervised approach which follows.

[0.485]{} ![Supervised neural network classifier output (a) and 