Hostname: page-component-848d4c4894-ndmmz Total loading time: 0 Render date: 2024-05-25T10:01:16.962Z Has data issue: false hasContentIssue false

Discovering exotic AGN behind the Magellanic Clouds

Published online by Cambridge University Press:  29 January 2021

Clara M. Pennock
Affiliation:
Lennard-Jones Laboratories, Keele University, Keele, ST5 5BG, UK email: c.m.pennock@keele.ac.uk
Jacco Th. van Loon
Affiliation:
Lennard-Jones Laboratories, Keele University, Keele, ST5 5BG, UK email: c.m.pennock@keele.ac.uk
Cameron P. M. Bell
Affiliation:
Leibniz Institute for Astrophysics Potsdam, Potsdam, Germany
Miroslav D. Filipović
Affiliation:
Western Sydney University, Sydney, Australia
Tana D. Joseph
Affiliation:
University of Manchester, Manchester, UK
Eleni Vardoulaki
Affiliation:
Max-Planck-Institut für Radioastronomie, Bonn, Germany
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The nearby Magellanic Clouds system covers more than 200 square degrees on the sky. Much of it has been mapped across the electromagnetic spectrum at high angular resolution and sensitivity –X-ray (XMM-Newton), UV (UVIT), optical (SMASH), IR (VISTA, WISE, Spitzer, Herschel), radio (ATCA, ASKAP, MeerKAT). This provides us with an excellent dataset to explore the galaxy populations behind the stellar-rich Magellanic Clouds. We seek to identify and characterise AGN via machine learning algorithms on this exquisite data set. Our project focuses not on establishing sequences and distributions of common types of galaxies and active galactic nuclei (AGN), but seeks to identify extreme examples, building on the recent accidental discoveries of unique AGN behind the Magellanic Clouds.

Type
Contributed Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of International Astronomical Union

References

Bianchi, L., Shiao, B., & Thilker, D., 2017, APJS, 230, 24 10.3847/1538-4365/aa7053CrossRefGoogle Scholar
Breiman, L., 2001, Machine Learning, 45, 1 Google Scholar
Cioni, M.-R. L., Clementini, G., Girardi, L., et al., 2011, AAP, 527, A116 10.1051/0004-6361/201016137CrossRefGoogle Scholar
Gaia, Collaboration, et al. 2018, AAP, 616, A1 10.1051/0004-6361/201833051CrossRefGoogle Scholar
Gordon, K. D., Meixner, M., Meade, M. R., et al., 2011, AJ, 142, 102 10.1088/0004-6256/142/4/102CrossRefGoogle Scholar
Hony, S., Kemper, F., Woods, P. M., et al., 2011, AAP, 531, A137 10.1051/0004-6361/201116845CrossRefGoogle Scholar
Joseph, T. D., Filipović, M. D., et al., 2019, MNRAS, 490, 1202 10.1093/mnras/stz2650CrossRefGoogle Scholar
van Loon, J. T., & Sansom, A. E., 2015, MNRAS, 453, 2341 10.1093/mnras/stv1787CrossRefGoogle Scholar
Meixner, M., Gordon, K. D., Indebetouw, R., et al., 2006, AJ, 132, 2268 10.1086/508185CrossRefGoogle Scholar
Norris, R. P., Hopkins, A. M., Afonso, J., et al., 2011, PASA, 28, 215 10.1071/AS11021CrossRefGoogle Scholar
Peng, C.Y., Ho, L.C., Impey, C.D., & Rix, H.-W., 2002, AJ, 124, 266 10.1086/340952CrossRefGoogle Scholar
Reis, I., & Baron, D. & Shahaf, S., 2018, AJ, 157, 16 10.3847/1538-3881/aaf101CrossRefGoogle Scholar
Schlafly, E. F., Meisner, A. M., & Green, G. M., 2019, APJS, 240, 30 10.3847/1538-4365/aafbeaCrossRefGoogle Scholar
Sturm, R., Haberl, F., Pietsch, W., et al., 2013, AAP, 558, A3 10.1051/0004-6361/201219935CrossRefGoogle Scholar