NDLI: Automatic image classification from Cherenkov telescopes using Bayesian ensemble of neural networks

Content Provider	IEEE Xplore Digital Library
Author	Malagon, C. Barrio, J.A. Nieto, D.
Copyright Year	2009
Description	Author affiliation: Dpto. Física Atómica, Univ. Complutense, 28040 Madrid, Spain (Barrio, J.A.; Nieto, D.) \|\| Dpto. Ingeniería Informática, Univ. Antonio de Nebrija, 28040 Madrid, Spain (Malagon, C.)
Abstract	The problem of identifying cosmic gamma ray events out of charged cosmic ray background in Cherenkov telescopes is one of the key problems in Very High Energy gamma ray astronomy. Separation between gamma-like and hadron-like events is performed by a Bayesian ensemble of neural networks and Markov chain Monte Carlo methods for model parameters optimization. The results are discussed in terms of the energy of the primaries and a complete study is made by using various data representation methods with different levels of feature reduction. Our classifier clearly outperforms the results obtained using standard feedforward neural networks, and its performance is comparable with random forests, which is actually used in data analysis of the MAGIC Cherenkov telescope. Regarding the energy of the primaries, it achieves very promising results in terms of classification accuracy with low energy events, the most difficult and unexplored energy range which will be a major issue in future explorations.
Starting Page	49
Ending Page	54
File Size	397346
Page Count	6
File Format	PDF
ISBN	9781424450541
DOI	10.1109/SOFA.2009.5254880
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-29
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Earth Monte Carlo methods Machine learning algorithms Bayesian methods Gamma ray detectors Neural networks Telescopes Gamma ray detection Gamma ray bursts Image classification
Content Type	Text
Resource Type	Article

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