Classification of Planetary Nebulae through Deep Transfer Learning

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 pr...

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Bibliographic Details
Published in:Galaxies 2020-12, Vol.8 (4), p.88
Main Authors: Awang Iskandar, Dayang N. F., Zijlstra, Albert A., McDonald, Iain, Abdullah, Rosni, Fuller, Gary A., Fauzi, Ahmad H., Abdullah, Johari
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Classification
Correlation coefficient
Correlation coefficients
Datasets
Deep learning
HASH DB
Image classification
Machine learning
Morphology
Nebulae
Neural networks
Observatories
Planetary nebulae
Space telescopes
Spectrum analysis
Stars & galaxies
transfer learning
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Summary: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.
ISSN:2075-4434
2075-4434
DOI:10.3390/galaxies8040088