  Density Estimation Trees (DETs) are decision trees trained on a multivariate
dataset to estimate its probability density function. While not competitive
with kernel techniques in terms of accuracy, they are incredibly fast,
embarrassingly parallel and relatively small when stored to disk. These
properties make DETs appealing in the resource-expensive horizon of the LHC
data analysis. Possible applications may include selection optimization, fast
simulation and fast detector calibration. In this contribution I describe the
algorithm, made available to the HEP community in a RooFit implementation. A
set of applications under discussion within the LHCb Collaboration are also
briefly illustrated.
