Classification of Planetary Nebulae through Deep Transfer Learning

This study investigate the effectiveness of using deep learning for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, and on 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 from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS).We find 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 themost effective deep learning algorithm. For themorphological classification we find 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 utilised to classify wide-field astronomical images.