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Article

Classification of Planetary Nebulae through Deep Transfer Learning

1
Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Sarawak 94300, Malaysia
2
Jodrell Bank Centre for Astrophysics, Department of Physics and Astronomy, School of Natural Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK
3
School of Physical Sciences, The Open University, Walton Hall, Kents Hill, Milton Keynes MK7 6AA, UK
4
School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Galaxies 2020, 8(4), 88; https://doi.org/10.3390/galaxies8040088
Received: 11 August 2020 / Revised: 6 December 2020 / Accepted: 7 December 2020 / Published: 11 December 2020
This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. This study was conducted using images from the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform database (HASH DB) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). We found that the algorithm has high success in distinguishing True PNe from other types of objects even without any parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis shows that DenseNet201 is the most effective DL algorithm. For the morphological classification, we found for three classes, Bipolar, Elliptical and Round, half of objects are correctly classified. Further improvement may require more data and/or training. We discuss the trade-offs and potential avenues for future work and conclude that deep transfer learning can be utilized to classify wide-field astronomical images.
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Keywords: deep learning; transfer learning; planetary nebulae; morphology; classification; HASH DB; Pan-STARRS deep learning; transfer learning; planetary nebulae; morphology; classification; HASH DB; Pan-STARRS
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MDPI and ACS Style

Awang Iskandar, D.N.F.; Zijlstra, A.A.; McDonald, I.; Abdullah, R.; Fuller, G.A.; Fauzi, A.H.; Abdullah, J. Classification of Planetary Nebulae through Deep Transfer Learning. Galaxies 2020, 8, 88. https://doi.org/10.3390/galaxies8040088

AMA Style

Awang Iskandar DNF, Zijlstra AA, McDonald I, Abdullah R, Fuller GA, Fauzi AH, Abdullah J. Classification of Planetary Nebulae through Deep Transfer Learning. Galaxies. 2020; 8(4):88. https://doi.org/10.3390/galaxies8040088

Chicago/Turabian Style

Awang Iskandar, Dayang N. F., Albert A. Zijlstra, Iain McDonald, Rosni Abdullah, Gary A. Fuller, Ahmad H. Fauzi, and Johari Abdullah. 2020. "Classification of Planetary Nebulae through Deep Transfer Learning" Galaxies 8, no. 4: 88. https://doi.org/10.3390/galaxies8040088

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