Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-17T17:22:46.907Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting the global far-infrared emission of galaxies

Published online by Cambridge University Press:  10 June 2020

Wouter Dobbels Open the ORCID record for Wouter Dobbels [Opens in a new window]  and
Maarten Baes Open the ORCID record for Maarten Baes [Opens in a new window]
Wouter Dobbels
Affiliation:
Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281, B-9000 Gent, Belgium email: wouter.dobbels@ugent.be
Maarten Baes
Affiliation:
Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281, B-9000 Gent, Belgium email: wouter.dobbels@ugent.be
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.

Dust absorbs stellar emission and reradiates this energy in the far-infrared (FIR). FIR observations hence give us a direct view of the dust, and allow us to study its properties. Unfortunately, FIR observations are only available for a small subset of galaxies. In this work, we estimate the global FIR emission from global UV-NIR observations. We show that a machine learning method clearly outperforms a SED modelling approach. For each galaxy, we not only predict the FIR flux across the 6 Herschel bands, but also estimate individual uncertainties. We inspect the worst predictions, and investigate how the machine learning predictor generalizes on new data. Our predictor can be used as a virtual observatory, which is especially useful now that there is still no confirmed next-generation FIR telescope.

Keywords

infrared: galaxiesinfrared: ISMgalaxies: ISM
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. 114 - 118
Copyright
© International Astronomical Union 2020

References

Bell, E. F. & de Jong, R. S. 2001, ApJ, 550, 21210.1086/319728CrossRefGoogle Scholar
Boquien, M., Burgarella, D., Roehlly, Y., Buat, V., Ciesla, L., Corre, D. Inoue, A. K., Salas, H., et al. 2018, arXiv:1811.03094Google Scholar
Charlot, S. & Fall, S. M. 2000, ApJ, 539, 718CrossRefGoogle Scholar
Ciesla, L., Boselli, A., Elbaz, D., Boissier, S., Buat, V., Charmandaris, V.Schreiber, C., Béthermin, M., et al. 2016, A&A, 585, A43Google Scholar
Clark, C. J. R., Verstocken, S., Bianchi, S., Fritz, J., Viaene, S., Smith, M. W. L., Baes, M., Casasola, V., et al. 2018, A&A, 609, A37Google Scholar
Conroy, C. 2013, ARAA, 51, 393CrossRefGoogle Scholar
Davies, J. I., Baes, M., Bianchi, S., Jones, A., Madden, S., Xilouris, M., Bocchio, M., Casasola, V., et al. 2017, PASP, 129, 044102CrossRefGoogle Scholar
De Geyter, G., Baes, M., Camps, P., Fritz, J., De Looze, I., Hughes, T. M., Viaene, S., Gentile, G., et al. 2014, MNRAS, 441, 869CrossRefGoogle Scholar
Driver, S. P., Hill, D. T., Kelvin, L. S., Robotham, A. S. G., Liske, J., Norberg, P., Baldry, I. K., Bamford, S. P., et al. 2011, MNRAS, 413, 971CrossRefGoogle Scholar
Eales, S., Dunne, L., Clements, D., Cooray, A., De Zotti, G., Dye, S., Ivison, R., Jarvis, M., et al. 2010, PASP, 122, 49910.1086/653086CrossRefGoogle Scholar
Gurevich, P. & Stuke, H. 2017, arXiv:1707.07287Google Scholar
Jones, A. P., Köhler, M., Ysard, N., Bocchio, M., & Verstraete, L. 2017, A&A, 602, A46Google Scholar
Kingma, D. P. & Ba, J. 2014, arXiv:1412.6980Google Scholar
Leja, J., Johnson, B. D., Conroy, C., van Dokkum, P. G., & Byler, N. 2017, ApJ, 837, 170CrossRefGoogle Scholar
Valiante, E., Smith, M. W. L., Eales, S., Maddox, S. J., Ibar, E., Hopwood, R., Dunne, L., Cigan, P. J., et al. 2016, MNRAS, 462, 3146CrossRefGoogle Scholar