Application of the Boosted Decision Tree Algorithmto Waveform Discrimination

Sammanfattning: The Polarised Gamma-ray Observer (PoGOLite) is a balloon-borne experiment aimed at measuring the polarisation of hard X-rays from astronomical sources. In the planned flight environment the neutron background is high. A smaller version of PoGOLite, named PoGOLino, was constructed with the goal of measuring the neutron background rates and was launched in March 2013. The signals produced in the detectors of both these instruments give rise to waveforms of different shapes depending on the type of detector the interaction occurred in. A method to distinguish between signal and background waveforms based on their shape has been developed. This was done using a machine learning algorithm called boosted decision trees, implemented in the software package Toolkit for Multivariate Data Analysis (TMVA). By constructing new discriminating variables the classification efficiency was improved. The developed classification will be applied to the measurements taken during the 2013 flight of PoGOLino and the method can also be used for the data analysis of future PoGOLite measurements.