Radio galaxy zoo: New giant radio galaxies in the RGZ DR1 catalogue

H. Tang; A. M.M. Scaife; O. I. Wong; A. D. Kapińska; L. Rudnick; S. S. Shabala; N. Seymour; R. P. Norris

doi:10.1093/mnras/staa2805

Radio galaxy zoo: New giant radio galaxies in the RGZ DR1 catalogue

H. Tang^*, A. M.M. Scaife, O. I. Wong, A. D. Kapińska, L. Rudnick, S. S. Shabala, N. Seymour, R. P. Norris

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

In this paper, we present the identification of five previously unknown giant radio galaxies (GRGs) using Data Release 1 of the Radio Galaxy Zoo citizen science project and a selection method appropriate to the training and validation of deep learning algorithms for new radio surveys. We associate one of these new GRGs with the brightest cluster galaxy (BCG) in the galaxy cluster GMBCG J251.67741+36.45295 and use literature data to identify a further 13 previously known GRGs as BCG candidates, increasing the number of known BCG GRGs by > 60 per cent. By examining local galaxy number densities for the number of all known BCG GRGs, we suggest that the existence of this growing number implies that GRGs are able to reside in the centres of rich (∼10¹⁴ M☉) galaxy clusters and challenges the hypothesis that GRGs grow to such sizes only in locally underdense environments.

Original language	English
Pages (from-to)	68-76
Number of pages	9
Journal	Monthly Notices of the Royal Astronomical Society
Volume	499
Issue number	1
DOIs	https://doi.org/10.1093/mnras/staa2805
Publication status	Published - 1 Nov 2020
Externally published	Yes

Keywords

Catalogues
Methods: data analysis
Radio continuum: galaxies

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10.1093/mnras/staa2805

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title = "Radio galaxy zoo: New giant radio galaxies in the RGZ DR1 catalogue",

abstract = "In this paper, we present the identification of five previously unknown giant radio galaxies (GRGs) using Data Release 1 of the Radio Galaxy Zoo citizen science project and a selection method appropriate to the training and validation of deep learning algorithms for new radio surveys. We associate one of these new GRGs with the brightest cluster galaxy (BCG) in the galaxy cluster GMBCG J251.67741+36.45295 and use literature data to identify a further 13 previously known GRGs as BCG candidates, increasing the number of known BCG GRGs by > 60 per cent. By examining local galaxy number densities for the number of all known BCG GRGs, we suggest that the existence of this growing number implies that GRGs are able to reside in the centres of rich (∼1014 M☉) galaxy clusters and challenges the hypothesis that GRGs grow to such sizes only in locally underdense environments.",

keywords = "Catalogues, Methods: data analysis, Radio continuum: galaxies",

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year = "2020",

month = nov,

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doi = "10.1093/mnras/staa2805",

language = "English",

volume = "499",

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journal = "Monthly Notices of the Royal Astronomical Society",

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T2 - New giant radio galaxies in the RGZ DR1 catalogue

AU - Tang, H.

AU - Scaife, A. M.M.

AU - Wong, O. I.

AU - Kapińska, A. D.

AU - Rudnick, L.

AU - Shabala, S. S.

AU - Seymour, N.

AU - Norris, R. P.

PY - 2020/11/1

Y1 - 2020/11/1

N2 - In this paper, we present the identification of five previously unknown giant radio galaxies (GRGs) using Data Release 1 of the Radio Galaxy Zoo citizen science project and a selection method appropriate to the training and validation of deep learning algorithms for new radio surveys. We associate one of these new GRGs with the brightest cluster galaxy (BCG) in the galaxy cluster GMBCG J251.67741+36.45295 and use literature data to identify a further 13 previously known GRGs as BCG candidates, increasing the number of known BCG GRGs by > 60 per cent. By examining local galaxy number densities for the number of all known BCG GRGs, we suggest that the existence of this growing number implies that GRGs are able to reside in the centres of rich (∼1014 M☉) galaxy clusters and challenges the hypothesis that GRGs grow to such sizes only in locally underdense environments.

AB - In this paper, we present the identification of five previously unknown giant radio galaxies (GRGs) using Data Release 1 of the Radio Galaxy Zoo citizen science project and a selection method appropriate to the training and validation of deep learning algorithms for new radio surveys. We associate one of these new GRGs with the brightest cluster galaxy (BCG) in the galaxy cluster GMBCG J251.67741+36.45295 and use literature data to identify a further 13 previously known GRGs as BCG candidates, increasing the number of known BCG GRGs by > 60 per cent. By examining local galaxy number densities for the number of all known BCG GRGs, we suggest that the existence of this growing number implies that GRGs are able to reside in the centres of rich (∼1014 M☉) galaxy clusters and challenges the hypothesis that GRGs grow to such sizes only in locally underdense environments.

KW - Catalogues

KW - Methods: data analysis

KW - Radio continuum: galaxies

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VL - 499

SP - 68

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JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

IS - 1

ER -

Radio galaxy zoo: New giant radio galaxies in the RGZ DR1 catalogue

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Keywords

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