| Issue |
EPJ Web Conf.
Volume 137, 2017
XIIth Quark Confinement and the Hadron Spectrum
|
|
|---|---|---|
| Article Number | 11009 | |
| Number of page(s) | 8 | |
| Section | Statistical Methods for Physics Analysis in the XXI Century | |
| DOI | https://doi.org/10.1051/epjconf/201713711009 | |
| Published online | 22 March 2017 | |
https://doi.org/10.1051/epjconf/201713711009
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating
1 Universidad de Oviedo, Spain
2 INFN, Padova, Italy
a e-mail: pietro.vischia@cern.ch
b e-mail: tommaso.dorigo@pd.infn.it
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
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