Classification of Planetary Nebulae through Deep Transfer Learning

TL;DR

This paper demonstrates that deep transfer learning, especially DenseNet201, effectively classifies planetary nebulae and their morphology from astronomical images, achieving high accuracy in distinguishing true PNe from other objects.

Contribution

It applies deep transfer learning with pre-trained ImageNet models to classify planetary nebulae and their morphology, showing promising results without extensive parameter tuning.

Findings

High success in distinguishing true PNe with a Matthews correlation coefficient of 0.9.

DenseNet201 outperforms other algorithms in classification tasks.

Approximately 50% accuracy in morphological classification for Bipolar, Elliptical, and Round classes.

Abstract

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.