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HST WFC3/Grism observations of the candidate ultra-high-redshift radio galaxy GLEAM J0917–0012

Published online by Cambridge University Press:  12 April 2022

N. Seymour Open the ORCID record for N. Seymour [Opens in a new window] ,
G. Drouart Open the ORCID record for G. Drouart [Opens in a new window] ,
G. Noirot ,
J. W. Broderick ,
R. J. Turner ,
S. S. Shabala ,
D. K. Stern ,
S. Bellstedt ,
S. Driver  and
L. Davies
...Show all authors
N. Seymour*
Affiliation:
International Centre for Radio Astronomy Research, Curtin University, 1 Turner Avenue, Bentley, WA 6102, Australia
G. Drouart
Affiliation:
International Centre for Radio Astronomy Research, Curtin University, 1 Turner Avenue, Bentley, WA 6102, Australia
G. Noirot
Affiliation:
Department of Astronomy and Physics, Institute for Computational Astrophysics, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3, Canada
J. W. Broderick
Affiliation:
International Centre for Radio Astronomy Research, Curtin University, 1 Turner Avenue, Bentley, WA 6102, Australia
R. J. Turner
Affiliation:
School of Natural Sciences, University of Tasmania, Private Bag 37, Hobart 7001, Australia
S. S. Shabala
Affiliation:
School of Natural Sciences, University of Tasmania, Private Bag 37, Hobart 7001, Australia
D. K. Stern
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
S. Bellstedt
Affiliation:
International Centre for Radio Astronomy Research, The University of Western Australia, 7 Fairway, Crawley, WA 6009, Australia
S. Driver
Affiliation:
International Centre for Radio Astronomy Research, The University of Western Australia, 7 Fairway, Crawley, WA 6009, Australia
L. Davies
Affiliation:
International Centre for Radio Astronomy Research, The University of Western Australia, 7 Fairway, Crawley, WA 6009, Australia
C. A. De Breuck
Affiliation:
European Southern Observatory, Karl Schwarzschild Strasse, D-85748 Garching bei München, Germany
J. A. Afonso
Affiliation:
Instituto de Astrofísica e Ciências do Espaço, Faculdade de Ciências, Universidade de Lisboa, OAL, Tapada da Ajuda, PT1349-018 Lisboa, Portugal Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Edifício C8, Campo Grande, PT1749-016 Lisbon, Portugal
J. D. R. Vernet
Affiliation:
European Southern Observatory, Karl Schwarzschild Strasse, D-85748 Garching bei München, Germany
T. J. Galvin
Affiliation:
International Centre for Radio Astronomy Research, Curtin University, 1 Turner Avenue, Bentley, WA 6102, Australia
*
Corresponding author: N. Seymour, email: nick.seymour@curtin.edu.au
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Abstract

We present Hubble Space Telescope Wide Field Camera 3 photometric and grism observations of the candidate ultra-high-redshift ($z>7$) radio galaxy, GLEAM J0917–0012. This radio source was selected due to the curvature in its 70–230 MHz, low-frequency Murchison Widefield Array radio spectrum and its faintness in K-band. Follow-up spectroscopic observations of this source with the Jansky Very Large Array and Atacama Large Millimetre Array were inconclusive as to its redshift. Our F105W and F0986M imaging observations detect the host of GLEAM J0917–0012 and a companion galaxy, $\sim$ one arcsec away. The G102 grism observations reveal a single weak line in each of the spectra of the host and the companion. To help identify these lines we utilised several photometric redshift techniques including template fitting to the grism spectra, fitting the ultraviolet (UV)-to-radio photometry with galaxy templates plus a synchrotron model, fitting of the UV-to-near-infrared photometry with EAZY, and fitting the radio data alone with RAiSERed. For the host of GLEAM J0917–0012 we find a line at $1.12\,\mu$m and the UV-to-radio spectral energy distribution (SED) fitting favours solutions at $z\sim 2$ or $z\sim 8$. While this fitting shows a weak preference for the lower redshift solution, the models from the higher redshift solution are more consistent with the strength of the spectral line. The redshift constraint by RAiSERed of $>6.5$ also supports the interpretation that this line could be Lyman$-\alpha$ at $z=8.21$; however EAZY favours the $z\sim 2$ solution. We discuss the implications of both solutions. For the companion galaxy we find a line at $0.98\,\mu$m and the SED fitting favours solutions at $z<3$ implying that the line could be the [OII]$\lambda3727$ doublet at $z=1.63$ (although the EAZY solution is $z\sim 2.6\pm 0.5$). Further observations are still required to unambiguously determine the redshift of this intriguing candidate ultra-high-redshift radio galaxy.

Keywords

radio galaxiesearly universeactive galactic nuclei
Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 39 , 2022 , e016
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Astronomical Society of Australia

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References

Aihara, H., et al. 2019, PASJ, 71, 114 Google Scholar
Akaike, H. 1974, IEEE TAC, 19, 716CrossRefGoogle Scholar
Álvarez-Márquez, J., Burgarella, D., Buat, V., Ilbert, O., & Pérez-González, P. G. 2019, A&A, 630, A153 CrossRefGoogle Scholar
Bañados, E., et al. 2016, ApJS, 227, 11 CrossRefGoogle Scholar
Bañados, E., et al. 2021, ApJ, 909, 80 CrossRefGoogle Scholar
Becker, R. H., White, R. L., & Helfand, D. J. 1995, 450, 559 CrossRefGoogle Scholar
Bellstedt, S., et al. 2021, MNRAS, 503, 3309 CrossRefGoogle Scholar
Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 CrossRefGoogle Scholar
Bertin, E., et al. 2002, in Astronomical Society of the Pacific Conference Series, Vol. 281, Astronomical Data Analysis Software and Systems XI, ed. Bohlender, D. A., Durand, D., & Handley, T. H., 228Google Scholar
Bicknell, G. V., Mukherjee, D., Wagner, A. Y., Sutherland, R. S., & Nesvadba, N. P. H. 2018, MNRAS, 475, 3493 CrossRefGoogle Scholar
Brammer, G. 2019, Grizli: Grism redshift and line analysis software, ascl:1905.001Google Scholar
Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503 CrossRefGoogle Scholar
Bruni, G., et al. 2019, ApJ, 875, 88 CrossRefGoogle Scholar
Callingham, J. R., et al. 2015, ApJ, 809, 168 Google Scholar
Callingham, J. R., et al. 2017, ApJ, 836, 174 Google Scholar
Carilli, C. L., Gnedin, N., Furlanetto, S., & Owen, F. 2004, 48, 1053 CrossRefGoogle Scholar
Ciardi, B., et al. 2015, MNRAS, 453, 101 Google Scholar
Condon, J. J., et al. 1998, AJ, 115, 1693 CrossRefGoogle Scholar
Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833 CrossRefGoogle Scholar
Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486 CrossRefGoogle Scholar
Croton, D. J., et al. 2016, ApJS, 222, 22 CrossRefGoogle Scholar
De Breuck, C., et al. 2010, ApJ, 725, 36 CrossRefGoogle Scholar
Driver, S. P., et al. 2011, MNRAS, 413, 971,Google Scholar
Driver, S. P., et al. 2016, MNRAS, 455, 3911 Google Scholar
Drouart, G., & Falkendal, T. 2018, MNRAS, 477, 4981 Google Scholar
Drouart, G., et al. 2014, A&A, 566, A53 Google Scholar
Drouart, G., et al. 2020, PASA, 37, e026 Google Scholar
Drouart, G., et al. 2021, PASA, 38, e049 Google Scholar
Edge, A., et al. 2013, Msngr, 154, 32 CrossRefGoogle Scholar
Fioc, M., & Rocca-Volmerange, B. 2019, A&A, 623, A143 CrossRefGoogle Scholar
Gaia Collaboration, et al. 2016, A&A, 595, A1 Google Scholar
Gaia Collaboration, et al. 2018, A&A, 616, A1 Google Scholar
Ghisellini, G., Celotti, A., Tavecchio, F., Haardt, F., & Sbarrato, T. 2014, MNRAS, 438, 2694 Google Scholar
Girardi, M., & Giuricin, G. 2000, ApJ, 540, 45 Google Scholar
Hardcastle, M. J., Lawrence, C. R., & Worrall, D. M. 1998, 504, 743 CrossRefGoogle Scholar
Hurley-Walker, N., et al. 2017, MNRAS, 464, 1146 Google Scholar
Ighina, L., et al. 2021, A&A, 647, L11 Google Scholar
Intema, H. T., Jagannathan, P., Mooley, K. P., & Frail, D. A. 2017, A&A, 598, A78 Google Scholar
Jaffe, W. J., & Perola, G. C. 1973, A&A, 26, 423 Google Scholar
King, E. A. 1994, PhD thesis, University of Tasmania, AustraliaGoogle Scholar
Kissler-Patig, M., et al. 2008, A&A, 491, 941 CrossRefGoogle Scholar
Komissarov, S. S., & Gubanov, A. G. 1994, A&A, 285, 27 CrossRefGoogle Scholar
Lacy, M., et al. 2020, PASP, 132, 035001 Google Scholar
Laporte, N., et al. 2021, MNRAS, 505, 3336 CrossRefGoogle Scholar
Laporte, N., et al. 2017, ApJ, 837, L21 Google Scholar
Martin, D. C., et al. 2005, ApJ, 619, L1 Google Scholar
Miley, G., & De Breuck, C. 2008, A&AR, 15, 67 Google Scholar
Nesvadba, N. P. H., et al. 2017, A&A, 600, A121 Google Scholar
O’Dea, C. P., & Baum, S. A. 1997, AJ, 113, 148 CrossRefGoogle Scholar
Oesch, P. A., et al. 2016, ApJ, 819, 129,CrossRefGoogle Scholar
Peterson, B. A., Savage, A., Jauncey, D. L., & Wright, A. E. 1982, ApJ, 260, L27 CrossRefGoogle Scholar
Pilbratt, G. L., et al. 2010, A&A, 518, L1 CrossRefGoogle Scholar
Planck Collaboration, et al. 2016, A&A, 594, A13 Google Scholar
Raouf, M., Shabala, S. S., Croton, D. J., Khosroshahi, H. G., & Bernyk, M. 2017, MNRAS, 471, 658 Google Scholar
Robotham, A. S. G., et al. 2018, MNRAS, 476, 3137 Google Scholar
Santos, S., Sobral, D., & Matthee, J. 2016, MNRAS, 463, 1678 CrossRefGoogle Scholar
Sawicki, M. 2012, PASP, 124, 1208 CrossRefGoogle Scholar
Saxena, A., et al. 2018a, MNRAS, 475, 5041 Google Scholar
Saxena, A., et al. 2018b, MNRAS, 480, 2733 Google Scholar
Schmidt, M. 1963, 197, 1040 CrossRefGoogle Scholar
Shimwell, T. W., et al. 2017, A&A, 598, A104 Google Scholar
Smith, M. W. L., et al. 2017, ApJS, 233, 26Google Scholar
Sosey, M. L., & Pirzkal, N. 2013, hstaxeGoogle Scholar
Taylor, M. B. 2005, in Astronomical Society of the Pacific Conference Series, Vol. 347, Astronomical Data Analysis Software and Systems XIV, ed. Shopbell, P., Britton, M., & Ebert, R., 29Google Scholar
Turner, R. J., Drouart, G., Seymour, N., & Shabala, S. S. 2020, MNRAS, 499, 3660 CrossRefGoogle Scholar
Turner, R. J., Rogers, J. G., Shabala, S. S., & Krause, M. G. H. 2018a, MNRAS, 473, 4179 CrossRefGoogle Scholar
Turner, R. J., & Shabala, S. S. 2015, ApJ, 806, 59 CrossRefGoogle Scholar
Turner, R. J., & Shabala, S. S. 2019, MNRAS, 486, 1225 CrossRefGoogle Scholar
Turner, R. J., Shabala, S. S., & Krause, M. G. H. 2018b, MNRAS, 474, 3361 CrossRefGoogle Scholar
Urry, C. M., & Padovani, P. 1995, 107, 803 CrossRefGoogle Scholar
van Breugel, W., et al. 1999, ApJ, 518, L61 CrossRefGoogle Scholar
Vernet, J., et al. 2001, A&A, 366, 7 CrossRefGoogle Scholar
Volonteri, M. 2012, Sci, 337, 544 Google Scholar
Wang, F., et al. 2021, ApJ, 907, L1 CrossRefGoogle Scholar
Wayth, R. B., et al. 2015, PASA, 32, e025 Google Scholar
Wilman, R. J., et al. 2008, MNRAS, 388, 1335 Google Scholar
Wright, E. L., et al. 2010, AJ, 140, 1868 Google Scholar