Year: 2020
Type: article
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.
Source: Galaxies
Authors: Dayang N. F. Awang Iskandar, Albert A. Zijlstra, Iain McDonald, Rosni Abdullah, Gary A. Fuller, Ahmad H. Fauzi, Johari Abdullah
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,056
Funders: National Science Foundation, Smithsonian Institution, National Aeronautics and Space Administration, Gordon and Betty Moore Foundation, Johns Hopkins University, Space Telescope Science Institute, University of Manchester, University of Edinburgh, Queen's University Belfast, Durham University, Queen's University, Universiti Sains Malaysia, National Central University, Universiti Malaysia Sarawak, Eötvös Loránd Tudományegyetem, Max-Planck-Institut für Astronomie, Smithsonian Astrophysical Observatory, Science Mission Directorate, Science and Technology Facilities Council, Planetary Science Division, Los Alamos National Laboratory
OpenAlex ID: W3113174703