  Analyses of collider data, often assisted by modern Machine Learning methods,
condense a number of observables into a few powerful discriminants for the
separation of the targeted signal process from the contributing backgrounds.
These discriminants are highly correlated with important physical observables;
using them in the event selection thus leads to the distortion of physically
relevant distributions. We present a novel method based on a differentiable
estimate of mutual information, a measure of non-linear dependency between
variables, to construct a discriminant that is statistically independent of a
number of selected observables, and so manages to preserve their distributions
in the event selection. Our strategy is evaluated in a realistic setting, the
analysis of the Standard Model Higgs boson decaying into a pair of bottom
quarks. Using the distribution of the invariant mass of the di-b-jet system to
extract the Higgs boson signal strength, our method achieves state-of-the-art
performance compared to other decorrelation techniques, while significantly
improving the sensitivity of a similar, cut-based, analysis published by ATLAS.
