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Testing star formation rate indicators using galaxy merger simulations and radiative transfer

Published online by Cambridge University Press:  13 April 2010

Christopher C. Hayward ,
Patrik Jonsson ,
Kai Noeske ,
Stijn Wuyts ,
T. J. Cox ,
Desika Narayanan ,
Brent Groves  and
Lars Hernquist
Christopher C. Hayward
Affiliation:
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA chayward@cfa.harvard.edu
Patrik Jonsson
Affiliation:
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA
Kai Noeske
Affiliation:
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA
Stijn Wuyts
Affiliation:
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA
T. J. Cox
Affiliation:
Carnegie Observatories, 813 Santa Barbara Street, Pasadena, CA 91101, USA
Desika Narayanan
Affiliation:
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA
Brent Groves
Affiliation:
Sterrewacht Leiden, Leiden University, Niels Bohrweg 2, Leiden 2333-CA, The Netherlands
Lars Hernquist
Affiliation:
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA
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Abstract

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We discuss our ongoing project analyzing N-body/smoothed-particle hydrodynamics simulations of isolated and merging galaxies, performed using GADGET-2 (Springel 2005), with the 3-D adaptive grid, polychromatic Monte Carlo radiative transfer code SUNRISE (Jonsson 2006). We apply commonly used UV, optical, and IR star formation rate (SFR) indicators to the integrated spectral energy distributions (SEDs) of the simulated galaxies in order to determine how well the SFR indicators recover the instantaneous SFR in the simulations. The models underlying each SFR indicator must necessarily make assumptions about physical properties of the galaxies, e.g., the star formation history (SFH), whereas all such properties are known in the simulations. This enables us to test and compare SFR indicators in a way that is complementary to observational studies. We present one preliminary result of interest: even after correcting the Hα luminosity for dust using the Calzetti et al. (2000) attenuation law the SFR is significantly underestimated for simulated galaxies with SFR ≳ 10 M⊙ yr−1.

Keywords

stars: formationgalaxies: stellar contentgalaxies: fundamental parametersgalaxies: starburstgalaxies: high-redshiftdustextinctionradiative transfer
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 5 , Symposium S262: Stellar Populations – Planning for the Next Decade , August 2009 , pp. 257 - 260
Copyright
Copyright © International Astronomical Union 2010

References

Bouwens, R. J., et al. 2009, arXiv:0909.4074Google Scholar
Brammer, G. B., et al. 2009, arXiv:0910.2227Google Scholar
Calzetti, D., Armus, L., Bohlin, R. C., Kinney, A. L., Koornneef, J., & Storchi-Bergmann, T. 2000, ApJ, 533, 682CrossRefGoogle Scholar
Cox, T. J., Dutta, S. N., Matteo, T. D., Hernquist, L., Hopkins, P. F., Robertson, B., & Springel, V. 2006, ApJ, 650, 791CrossRefGoogle Scholar
Daddi, E., et al. 2007, ApJ, 670, 156CrossRefGoogle Scholar
Davé, R., Finlator, K., Oppenheimer, B. D., Fardal, M., Katz, N., Kereš, D., & Weinberg, D. H. 2009, arXiv:0909.4078Google Scholar
Draine, B. T., & Li, A. 2007, ApJ, 657, 810CrossRefGoogle Scholar
Dwek, E. 1998, ApJ, 501, 643CrossRefGoogle Scholar
Groves, B., Dopita, M. A., Sutherland, R. S., Kewley, L. J., Fischera, J., Leitherer, C., Brandl, B., & van Breugel, W. 2008, ApJS, 176, 438CrossRefGoogle Scholar
Hopkins, P. F., Richards, G. T., & Hernquist, L. 2007, ApJ, 654, 731CrossRefGoogle Scholar
Jonsson, P. 2006, MNRAS, 372, 2CrossRefGoogle Scholar
Jonsson, P., Cox, T. J., Primack, J. R., & Somerville, R. S. 2006, ApJ, 637, 255CrossRefGoogle Scholar
Jonsson, P., Groves, B., & Cox, T. J. 2009, arXiv:0906.2156Google Scholar
Juvela, M. 2005, A&A, 440, 531Google Scholar
Kennicutt, R. C. 1998, ARAA, 36, 189CrossRefGoogle Scholar
Kroupa, P. 2001, MNRAS, 322, 231CrossRefGoogle Scholar
Leitherer, C., et al. 1999, ApJS, 123, 3CrossRefGoogle Scholar
Narayanan, D., Cox, T. J., Hayward, C., Younger, J. D., & Hernquist, L. 2009 a, arXiv:0905.2184Google Scholar
Narayanan, D., Hayward, C. C., Cox, T. J., Hernquist, L., Jonsson, P., Younger, J. D., & Groves, B. 2009 b, arXiv:0904.0004Google Scholar
Narayanan, D., et al. 2009 c, arXiv:0910.2234Google Scholar
Noeske, K. G., et al. 2007 a, ApJ, 660, L47CrossRefGoogle Scholar
Noeske, K. G., et al. 2007 b, ApJ, 660, L43CrossRefGoogle Scholar
Papovich, C., Rudnick, G., Rigby, J. R., Willmer, C. N. A., Smith, J.-D. T., Finkelstein, S. L., Egami, E., & Rieke, M. 2009, ApJ, 704, 1506CrossRefGoogle Scholar
Reddy, N. A. & Steidel, C. C. 2009, ApJ, 692, 778CrossRefGoogle Scholar
Smith, L. J., Norris, R. P. F., & Crowther, P. A. 2002, MNRAS, 337, 1309CrossRefGoogle Scholar
Springel, V. 2005, MNRAS, 364, 1105CrossRefGoogle Scholar
Springel, V. & Hernquist, L. 2003, MNRAS, 339, 289CrossRefGoogle Scholar
Springel, V., Matteo, T. D., & Hernquist, L. 2005, MNRAS, 361, 776CrossRefGoogle Scholar
Vázquez, G. A. & Leitherer, C. 2005, ApJ, 621, 695CrossRefGoogle Scholar
Weingartner, J. C. & Draine, B. T. 2001, ApJ, 548, 296CrossRefGoogle Scholar
Younger, J. D., Hayward, C. C., Narayanan, D., Cox, T. J., Hernquist, L., & Jonsson, P. 2009, MNRAS, 396, L66CrossRefGoogle Scholar