no code implementations • 21 Mar 2024 • Nikhel Gupta, Ray P. Norris, Zeeshan Hayder, Minh Huynh, Lars Petersson, X. Rosalind Wang, Andrew M. Hopkins, Heinz Andernach, Yjan Gordon, Simone Riggi, Miranda Yew, Evan J. Crawford, Bärbel Koribalski, Miroslav D. Filipović, Anna D. Kapinśka, Stanislav Shabala, Tessa Vernstrom, Joshua R. Marvil
The Gal-DINO network is trained and evaluated on approximately 5, 000 visually inspected radio galaxies and their infrared hosts, encompassing both compact and extended radio morphologies.
3 code implementations • 11 Dec 2023 • Nikhel Gupta, Zeeshan Hayder, Ray P. Norris, Minh Hyunh, Lars Petersson
We present a novel multimodal dataset developed by expert astronomers to automate the detection and localisation of multi-component extended radio galaxies and their corresponding infrared hosts.
3 code implementations • 1 Dec 2023 • Nikhel Gupta, Zeeshan Hayder, Ray P. Norris, Minh Huynh, Lars Petersson
Creating radio galaxy catalogues from next-generation deep surveys requires automated identification of associated components of extended sources and their corresponding infrared hosts.
Ranked #1 on 2D Object Detection on RadioGalaxyNET Dataset
1 code implementation • 9 Aug 2023 • Nikhel Gupta, Zeeshan Hayder, Ray P. Norris, Minh Huynh, Lars Petersson, X. Rosalind Wang, Heinz Andernach, Bärbel S. Koribalski, Miranda Yew, Evan J. Crawford
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.
no code implementations • 13 Mar 2020 • Nikhel Gupta, Christian L. Reichardt
We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky.