Hostname: page-component-848d4c4894-hfldf Total loading time: 0 Render date: 2024-06-04T07:39:15.133Z Has data issue: false hasContentIssue false

The High Mass X-ray binaries in star-forming galaxies

Published online by Cambridge University Press:  30 December 2019

M. Celeste Artale
Affiliation:
Institut für Astro- und Teilchenphysik, Universität Innsbruck, Technikerstrasse 25/8, 6020 Innsbruck, Austria
Nicola Giacobbo
Affiliation:
INAF, Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5, I–35122 Padova, Italy INFN, Milano Bicocca, Piazza della Scienza 3, I–20126, Milano, Italy Dipartimento di Fisica e Astronomia “G. Galilei”, Università di Padova, vicolo dell’Osservatorio 3, I-35122, Italy
Michela Mapelli
Affiliation:
Institut für Astro- und Teilchenphysik, Universität Innsbruck, Technikerstrasse 25/8, 6020 Innsbruck, Austria INAF, Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5, I–35122 Padova, Italy INFN, Milano Bicocca, Piazza della Scienza 3, I–20126, Milano, Italy
Paolo Esposito
Affiliation:
INAF–Istituto di Astrofisica Spaziale e Fisica Cosmica di Milano, via E. Bassini 15, 20133 Milano, Italy email: mcartale@gmail.com
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 high mass X-ray binaries (HMXBs) provide an exciting framework to investigate the evolution of massive stars and the processes behind binary evolution. HMXBs have shown to be good tracers of recent star formation in galaxies and might be important feedback sources at early stages of the Universe. Furthermore, HMXBs are likely the progenitors of gravitational wave sources (BH–BH or BH–NS binaries that may merge producing gravitational waves). In this work, we investigate the nature and properties of HMXB population in star-forming galaxies. We combine the results from the population synthesis model MOBSE (Giacobbo & Mapelli 2018a) together with galaxy catalogs from EAGLE simulation (Schaye et al. 2015). Therefore, this method describes the HMXBs within their host galaxies in a self-consistent way. We compute the X-ray luminosity function (XLF) of HMXBs in star-forming galaxies, showing that this methodology matches the main features of the observed XLF.

Type
Contributed Papers
Copyright
© International Astronomical Union 2019 

References

Artale, M. C., Tissera, P. B., Pellizza, L. J., et al. 2015, MNRAS, 448, 3071 CrossRefGoogle Scholar
Belczynski, K., Kalogera, V., Zezas, A., Fabbiano, G., et al. 2004, APJL, 601, 147 CrossRefGoogle Scholar
Chen, Y., Bressan, A., Girardi, L., Marigo, P., Kong, X., Lanza, A., et al. 2015, MNRAS, 452, 1068 CrossRefGoogle Scholar
Douna, V. M., Pellizza, L. J., Laurent, P., Mirabel, I. F., et al. 2018 MNRAS, 474, 3488 CrossRefGoogle Scholar
Fragos, T., Lehmer, B., Tremmel, M., Tzanavaris, P., Basu-Zych, A., Belczynski, K., Hornschemeier, A., Jenkins, L., Kalogera, V., Ptak, A., Zezas, A., et al. 2013, ApJ, 764, 41 CrossRefGoogle Scholar
Frank, J., King, A. & Raine, D. J. 2002, Accretion Power in Astrophysics ISBN 0521620538. Cambridge, UK. Cambridge University Press CrossRefGoogle Scholar
Fryer, C. L., Belczynski, K., Wiktorowicz, G., Dominik, M., Kalogera, V., Holz, D. E., et al. 2012, ApJ, 749, 91 CrossRefGoogle Scholar
Garratt-Smithson, L., Wynn, G. A., Power, C., Nixon, C. J., et al. 2018, MNRAS, 480, 2985 CrossRefGoogle Scholar
Giacobbo, N., Mapelli, M. & Spera, M. 2018a, MNRAS, 474, 2959 CrossRefGoogle Scholar
Giacobbo, N. & Mapelli, M. 2018b, preprint (arXiv:1805.11100)Google Scholar
Giacobbo, N. & Mapelli, M. 2018c, MNRAS, 480, 2011 CrossRefGoogle Scholar
Grimm, H.-J., Gilfanov, M., Sunyaev, R., et al. 2003, MNRAS, 339, 793 CrossRefGoogle Scholar
Hurley, J. R., Tout, C. A., Pols, O. R., et al. 2002, MNRAS 329, 897 CrossRefGoogle Scholar
Justham, S. & Schawinski, K. 2012, MNRAS, 423, 1641 CrossRefGoogle Scholar
Kaaret, P., Feng, H., Roberts, T. P., et al. 2017, ARAA, 55, 303 CrossRefGoogle Scholar
Lutovinov, A. A., Revnivtsev, M. G., Tsygankov, S. S., Krivonos, R. A., et al. 2013, MNRAS, 431, 327 CrossRefGoogle Scholar
Mapelli, M., Ripamonti, E., Zampieri, L., Colpi, M., Bressan, A., et al. 2010, MNRAS, 408, 234 CrossRefGoogle Scholar
Mapelli, M., Giacobbo, N., Ripamonti, E., Spera, M., et al. 2017, MNRAS, 472, 2422 CrossRefGoogle Scholar
Mapelli, M., & Giacobbo, N. 2018a, MNRAS, 479, 4391 CrossRefGoogle Scholar
Mapelli, M., Giacobbo, N., Toffano, M., et al. 2018b, arXiv:1809.03521Google Scholar
Mineo, S., Gilfanov, M., Sunyaev, R., et al. 2012, MNRAS, 419, 2095 CrossRefGoogle Scholar
Collaboration, Planck 2014, A&A, 571, 16 Google Scholar
Spera, M. & Mapelli, M. 2017, MNRAS, 470, 4739 CrossRefGoogle Scholar
Schaye, J., Crain, R. A., Bower, R. G., Furlong, M., Schaller, M., Theuns, T., Dalla Vecchia, C., Frenk, C. S., McCarthy, I. G., Helly, J. C., Jenkins, A., Rosas-Guevara, Y. M., et al. 2015, MNRAS, 446, 521 CrossRefGoogle Scholar
Vink, J. S., de Koter, A., Lamers, H. J. G. L. M., et al. 2001, A&A, 369, 574 Google Scholar
Vink, J. S., & de Koter, A. 2005, A&A, 442, 587 Google Scholar
Vulic, N., Hornschemeier, A. E., Wik, D. R., Yukita, M., Zezas, A., Ptak, A. F., Lehmer, B. D., et al. 2018, ApJ, 864, 150 CrossRefGoogle Scholar
Zuo, Z.-Y., Li, X.-D., Gu, Q.-S., et al. 2014, MNRAS, 437, 1187 CrossRefGoogle Scholar