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Deep learning for galaxy mergers in the galaxy main sequence

Published online by Cambridge University Press:  10 June 2020

William J. Pearson ,
Lingyu Wang ,
James Trayford ,
Carlo E. Petrillo  and
Floris F. S. van der Tak
William J. Pearson
Affiliation:
SRON Netherlands Institute for Space Research Landleven 12, 9747 AD, Groningen, The Netherlands email: [email protected] Kapteyn Astronomical Institute, University of Groningen Postbus 800, 9700 AV Groningen, The Netherlands
Lingyu Wang
Affiliation:
SRON Netherlands Institute for Space Research Landleven 12, 9747 AD, Groningen, The Netherlands email: [email protected] Kapteyn Astronomical Institute, University of Groningen Postbus 800, 9700 AV Groningen, The Netherlands
James Trayford
Affiliation:
Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands
Carlo E. Petrillo
Affiliation:
Kapteyn Astronomical Institute, University of Groningen Postbus 800, 9700 AV Groningen, The Netherlands
Floris F. S. van der Tak
Affiliation:
SRON Netherlands Institute for Space Research Landleven 12, 9747 AD, Groningen, The Netherlands email: [email protected] Kapteyn Astronomical Institute, University of Groningen Postbus 800, 9700 AV Groningen, The Netherlands
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Abstract

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Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim.

Deep learning techniques are applied to images from the Sloan Digital Sky Survey to provide visual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this classification is to split the galaxy population into merger and non-merger systems and we are currently achieving an accuracy of 92.5%. Stellar masses and star formation rates are also estimated using panchromatic data for the entire galaxy population. With these preliminary data, the mergers are placed onto the full galaxy main sequence, where we find that merging systems lie across the entire star formation rate - stellar mass plane.

Keywords

methods: data analysisgalaxies: evolutiongalaxies: interactionsgalaxies: starburstinfrared: galaxies
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S341: Challenges in Panchromatic Modelling with Next Generation Facilities , November 2019 , pp. 104 - 108
Copyright
© International Astronomical Union 2020

References

Ackermann, S., Schawinski, K., Zhang, C., Weigel, A., & Turp, M. 2018, MNRAS, 479, 415CrossRefGoogle Scholar
Boquien, M., Burgarella, D., Roehlly, Y., et al. 2018, ArXiv arXiv:1811.03094Google Scholar
Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151CrossRefGoogle Scholar
Cortijo-Ferrero, C., González Delgado, R. M., Pérez, E., et al. 2017, A&A, 607, A70Google Scholar
Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010a, MNRAS, 401, 1552CrossRefGoogle Scholar
Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010b, MNRAS, 401, 1043CrossRefGoogle Scholar
Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441CrossRefGoogle Scholar
Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astronomy and Geophysics, 50, 5.1210.1111/j.1468-4004.2009.50512.xGoogle Scholar
Griffin, M. J., Abergel, A., Abreu, A., et al. 2010, A&A, 518, L3Google ScholarPubMed
Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8CrossRefGoogle Scholar
Hurley, P. D., Oliver, S., Betancourt, M., et al. 2017, MNRAS, 464, 885CrossRefGoogle Scholar
Knapen, J. H. & Cisternas, M. 2015, ApJ, 807, L16CrossRefGoogle Scholar
Knapen, J. H., Cisternas, M., & Querejeta, M. 2015, MNRAS, 454, 1742CrossRefGoogle Scholar
Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv arXiv:1110.3193Google Scholar
Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166CrossRefGoogle Scholar
Science Collaboration, LSST, Abell, P. A., Allison, J., et al. 2009, ArXiv arXiv:0912.0201Google Scholar
Luo, W., Yang, X., & Zhang, Y. 2014, ApJ, 789, L16CrossRefGoogle Scholar
Man, A. W. S., Zirm, A. W., & Toft, S. 2016, ApJ, 830, 8910.3847/0004-637X/830/2/89CrossRefGoogle Scholar
Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43CrossRefGoogle Scholar
Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793Google Scholar
Pearson, W. J., Wang, L., Hurley, P. D., et al. 2018, A&A, 615, A146Google Scholar
Pearson, W. J., Wang, L., van der Tak, F. F. S., et al. 2017, A&A, 603, A102Google Scholar
Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 112910.1093/mnras/stx2052CrossRefGoogle Scholar
Pozzetti, L., Bolzonella, M., Zucca, E., et al. 2010, A&A, 523, A13Google Scholar
Sanders, D. B. & Mirabel, I. F. 1996, ARA&A, 34, 749CrossRefGoogle Scholar
Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521CrossRefGoogle Scholar
Schweizer, F. 2005, in Astrophysics and Space Science Library, Vol. 329, Starbursts: From 30 Doradus to Lyman Break Galaxies, ed. R. de Grijs & R. M. González Delgado, 143Google Scholar
Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15CrossRefGoogle Scholar
Tomczak, A. R., Quadri, R. F., Tran, K.-V. H., et al. 2016, ApJ, 817, 118CrossRefGoogle Scholar
York, D. G., Adelman, J., Anderson, J. E. Jr., et al. 2000, AJ, 120, 157910.1086/301513CrossRefGoogle Scholar