For data sets populated by a very well modeled process and by another process
of unknown probability density function (PDF), a desired feature when
manipulating the fraction of the unknown process (either for enhancing it or
suppressing it) consists in avoiding to modify the kinematic distributions of
the well modeled one. A bootstrap technique is used to identify sub-samples
rich in the well modeled process, and classify each event according to the
frequency of it being part of such sub-samples. Comparisons with general MVA
algorithms will be shown, as well as a study of the asymptotic properties of
the method, making use of a public domain data set that models a typical search
for new physics as performed at hadronic colliders such as the Large Hadron
Collider (LHC).Comment: 8 pages, 5 figures. Proceedings of the XIIth Quark Confinement and
Hadron Spectrum conference, 28/8-2/9 2016, Thessaloniki, Greec
