Abstract
In this paper we present a feature set for Gamma-ray and Background Hadron events automatic classification. We selected the best parameters combination collected by Cherenkov telescopes in order to make a robust Gamma-ray recognition against different signal noise levels using multiple Machine Learning approaches for pattern recognition. We made a comparison of the robustness to noise for four classifiers reaching an accuracy up to \(90.14\%\) in high noise level cases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abeysekara, A., et al.: Sensitivity of the high altitude water Cherenkov detector to sources of multi-TeV gamma rays. Astropart. Phys. 50, 26–32 (2013)
Atkins, R., et al.: Observation of TeV gamma rays from the Crab nebula with MILAGRO using a new background rejection technique. Astrophys. J. 595(2), 803 (2003)
Badran, H., Weekes, T.: Improvement of gamma-hadron discrimination at TeV energies using a new parameter, image surface brightness. Astropart. Phys. 7(4), 307–314 (1997)
Berge, D., Funk, S., Hinton, J.: Background modelling in very-high-energy \(\gamma \)-ray astronomy. Astron. Astrophys. 466(3), 1219–1229 (2007)
Bernlöhr, K., et al.: Monte Carlo design studies for the Cherenkov telescope array. Astropart. Phys. 43, 171–188 (2013)
Bourbeau, E., Capistrán, T., Torres, I., Moreno, E.: New gamma/hadron separation parameters for a neural network for HAWC. arXiv preprint arXiv:1708.03585 (2017)
Brun, R., Rademakers, F.: Root—an object oriented data analysis framework. Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrometers Detect. Assoc. Equip. 389(1–2), 81–86 (1997)
Grabski, V., Chilingarian, A., Nellen, L.: Gamma/hadron separation study for the HAWC detector on the basis of the multidimensional feature space using non parametric approach (2011)
Hampel-Arias, Z., et al.: Gamma hadron separation using pairwise compactness method with HAWC. arXiv preprint arXiv:1508.04047 (2015)
Krause, M., Pueschel, E., Maier, G.: Improved \(\gamma \)/hadron separation for the detection of faint \(\gamma \)-ray sources using boosted decision trees. Astropart. Phys. 89, 1–9 (2017)
Reynolds, P., et al.: Survey of candidate gamma-ray sources at TeV energies using a high-resolution cerenkov imaging system-1988-1991. Astrophys. J. 404, 206–218 (1993)
Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1–2), 23–69 (2003)
Westerhoff, S., et al.: Separating \(\gamma \)-and hadron-induced cosmic ray air showers with feed-forward neural networks using the charged particle information. Astropart. Phys. 4(2), 119–132 (1995)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Burgos-Madrigal, A., Ortiz-Esquivel, A.E., Díaz-Hernández, R., Altamirano-Robles, L. (2018). Feature Selection for Automatic Classification of Gamma-Ray and Background Hadron Events with Different Noise Levels. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-04491-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04490-9
Online ISBN: 978-3-030-04491-6
eBook Packages: Computer ScienceComputer Science (R0)