EPJ Web Conf.
Volume 137, 2017XIIth Quark Confinement and the Hadron Spectrum
|Number of page(s)||8|
|Section||Statistical Methods for Physics Analysis in the XXI Century|
|Published online||22 March 2017|
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating
1 Universidad de Oviedo, Spain
2 INFN, Padova, Italy
Published online: 22 March 2017
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).
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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