Deep learning for morphological identification of extended radio galaxies using weak labels
- OSTI
Abstract
The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35$$\mu$$Jy beam$$^{-1}$$. We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP$$_{50}$$of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link:https://github.com/Nikhel1/Gal-CAM
- Research Organization:
- Univ. of Pittsburgh, PA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- SC0007914
- OSTI ID:
- 2419772
- Journal Information:
- Publications of the Astronomical Society of Australia, Journal Name: Publications of the Astronomical Society of Australia Vol. 40; ISSN 1323-3580
- Publisher:
- CSIRO
- Country of Publication:
- United States
- Language:
- English
Similar Records
Large gyro-orbit model of ion velocity distribution in plasma near a wall in a grazing-angle magnetic field
Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive