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Application of the Boosted Decision Tree Algorithmto Waveform Discrimination
KTH, School of Engineering Sciences (SCI), Physics.
2013 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. , 37 p.
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-129408OAI: diva2:652319
Available from: 2013-09-30 Created: 2013-09-30 Last updated: 2013-09-30Bibliographically approved

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