Data

RadioGalaxyNET Dataset - Extended Radio Galaxies

Commonwealth Scientific and Industrial Research Organisation
Gupta, Nikhel ; Norris, Ray ; Huynh, Minh ; Hayder, Zeeshan ; Petersson, Lars
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/btk3-vx79&rft.title=RadioGalaxyNET Dataset - Extended Radio Galaxies&rft.identifier=https://doi.org/10.25919/btk3-vx79&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Automating the creation of catalogues for radio galaxies in next-generation deep surveys necessitates the identification of components within extended sources and their respective infrared hosts. We present RadioGalaxyNET, a multimodal dataset, tailored for machine learning tasks to streamline the automated detection and localization of multi-component extended radio galaxies and their associated infrared hosts. The dataset encompasses 4,155 instances of galaxies across 2,800 images, incorporating both radio and infrared channels. Each instance furnishes details about the extended radio galaxy class, a bounding box covering all components, a pixel-level segmentation mask, and the keypoint position of the corresponding infrared host galaxy. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, corresponding infrared images, and instance-level annotations for galaxy detection. For more details, see the RadioGalaxyNET paper in the PASA journal and the NeurIPS 2023 conference workshop.&rft.creator=Gupta, Nikhel &rft.creator=Norris, Ray &rft.creator=Huynh, Minh &rft.creator=Hayder, Zeeshan &rft.creator=Petersson, Lars &rft.date=2023&rft.edition=v1&rft_rights=Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2023.&rft_subject=galaxies: active&rft_subject=galaxies: peculiar&rft_subject=radio continuum: galaxies&rft_subject=Galaxy: evolution&rft_subject=methods: data analysis&rft_subject=Astronomical sciences not elsewhere classified&rft_subject=Astronomical sciences&rft_subject=PHYSICAL SCIENCES&rft_subject=Space sciences not elsewhere classified&rft_subject=Space sciences&rft.type=dataset&rft.language=English Access the data
Cite Saved to MyRDA Save to MyRDA

Licence & Rights:

Non-Commercial Licence view details
CC-BY-NC-SA

Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence
https://creativecommons.org/licenses/by-nc-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2023.

Access:

Open view details

Accessible for free

Contact Information



Full description

Automating the creation of catalogues for radio galaxies in next-generation deep surveys necessitates the identification of components within extended sources and their respective infrared hosts. We present RadioGalaxyNET, a multimodal dataset, tailored for machine learning tasks to streamline the automated detection and localization of multi-component extended radio galaxies and their associated infrared hosts. The dataset encompasses 4,155 instances of galaxies across 2,800 images, incorporating both radio and infrared channels. Each instance furnishes details about the extended radio galaxy class, a bounding box covering all components, a pixel-level segmentation mask, and the keypoint position of the corresponding infrared host galaxy. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, corresponding infrared images, and instance-level annotations for galaxy detection.

For more details, see the RadioGalaxyNET paper in the PASA journal and the NeurIPS 2023 conference workshop.

Available: 2023-12-01

This dataset is part of a larger collection

Click to explore relationships graph
Help

Related Data

Related People

Related Grants and Projects

Subjects
Astronomical Sciences | Astronomical Sciences Not Elsewhere Classified | Galaxy: evolution | Physical Sciences | Space Sciences | Space Sciences Not Elsewhere Classified | galaxies: active | galaxies: peculiar | methods: data analysis | radio continuum: galaxies |

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers
ACN 633 798 857