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arXiv · 1611.08256

The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating

Pietro Vischia·Tommaso Dorigo
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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).

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BibTeXRIS

Pietro Vischia, Tommaso Dorigo. 2016-11-24. The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating. https://doi.org/10.1051/epjconf/201713711009

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