The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating
Abstract
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).
Cite
@article{arxiv.1611.08256,
title = {The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating},
author = {Pietro Vischia and Tommaso Dorigo},
journal= {arXiv preprint arXiv:1611.08256},
year = {2017}
}
Comments
8 pages, 5 figures. Proceedings of the XIIth Quark Confinement and Hadron Spectrum conference, 28/8-2/9 2016, Thessaloniki, Greece