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Three Modes of Metal-Enriched Star Formation at High Redshift

Published online by Cambridge University Press:  01 June 2008

Britton D. Smith ,
Matthew J. Turk ,
Steinn Sigurdsson ,
Brian W. O'Shea  and
Michael L. Norman
Britton D. Smith
Affiliation:
Center for Astrophysics & Space Astronomy, Department of Astrophysical & Planetary Sciences, University of Colorado, Boulder, CO, 80309 email: brittons@origins.colorado.edu
Matthew J. Turk
Affiliation:
Kavli Institute for Particle Astrophysics and Cosmology, 2575 Sand Hill Rd., Mail Stop 29, Menlo Park, CA 94025 email: mturk@slac.stanford.edu
Steinn Sigurdsson
Affiliation:
Department of Astronomy & Astrophysics, 525 Davey Laboratory, The Pennsylvania State University, University Park, PA 16802 email: steinn@astro.psu.edu
Brian W. O'Shea
Affiliation:
Department of Physics & Astronomy, Michigan State University, East Lansing, MI 48824 email: bwoshea@lanl.gov
Michael L. Norman
Affiliation:
Center for Astrophysics and Space Sciences, University of California at San Diego, La Jolla, CA 92093 email: mlnorman@ucsd.edu
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Abstract

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It is generally accepted that the very first stars in the universe were significantly more massive and formed much more in isolation than stars observed today. This suggests that there was a transition in star formation modes that was most likely related to the metallicity of the star-forming environment. We study how the addition of heavy elements alters the dynamics of collapsing gas by performing a series of numerical simulations of primordial star formation with various levels of pre-enrichment, using the adaptive mesh refinement, hydrodynamic + N-body code, Enzo. At high redshifts, the process of star formation is heavily influenced by the cosmic microwave background (CMB), which creates a temperature floor for the gas. Our results show that cloud-collapse can follow three distinct paths, depending on the metallicity. For very low metallicities (log10(Z/Z⊙) < −3.5), star formation proceeds in the primordial mode, producing only massive, singular objects. For high metallicities (log10(Z/Z⊙) > −3), efficient cooling from the metals cools the gas to the CMB temperature when the core density is still very low. When the gas temperature reaches the CMB temperature, the core becomes very thermally stable, and further fragmentation is heavily suppressed. In our simulations with log10(Z/Z⊙) > −3, only a single object forms with a mass-scale of a few hundred M⊙. We refer to this as the CMB-regulated star formation mode. For metallicities between these two limits (−3.5 < log10(Z/Z⊙) < −3), the gas cools efficiently, but never reaches the CMB temperature. In this mode, termed the metallicity-regulated star formation mode, the minimum gas temperature is reached at much higher densities, allowing the core to fragment and form multiple objects with mass-scales of only a few M⊙. Our results imply that the stellar initial mass function was top-heavy at very high redshift due to stars forming in the CMB-regulated mode. As the CMB temperature lowers with time, the metallicity-regulated star formation mode (producing multiple low-mass stars) operates at higher metallicities and eventually becomes the sole mode of star formation.

Keywords

stars: formation(cosmology:) early universe
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 4 , Issue S255: Low-Metallicity Star Formation: From the First Stars to Dwarf Galaxies , June 2008 , pp. 111 - 115
Copyright
Copyright © International Astronomical Union 2008

References

Bromm, V. & Loeb, A. 2003, Nature, 425, 812CrossRefGoogle Scholar
Bryan, G. & Norman, M. L. 1997, in {Workshop on Structured Adaptive Mech Refinement Grid Methods}, ed. Chrisochoides, N., {IMA Volumes in Mathematics No. 117} (Springer-Verlag)Google Scholar
Evans II, N. J. 1999, ARAA, 37, 311CrossRefGoogle Scholar
Ferland, G. J., Korista, K. T., Verner, D. A., Ferguson, J. W., Kingdon, J. B., & Verner, E. M. 1998, PASP, 110, 761CrossRefGoogle Scholar
Larson, R. B. 1985, MNRAS, 214, 379CrossRefGoogle Scholar
Larson, R. B. 1998, MNRAS, 301, 569CrossRefGoogle Scholar
Larson, R. B. 2005, MNRAS, 359, 211CrossRefGoogle Scholar
O'Shea, B. W., G, B., Bordner, J., Norman, M. L., Abel, T., Harknes, R., & Kritsuk, A. 2004, in Lecture Notes in Computational Science and Engineering, Vol. 41, Adaptive Mesh Refinement - Theory and Applications, ed. Plewa, T., Linde, T., & Weirs, V. G.Google Scholar
O'Shea, B. W. & Norman, M. L. 2007, ApJ, 654, 66CrossRefGoogle Scholar
Santoro, F. & Shull, J. M. 2006, ApJ, 643, 26CrossRefGoogle Scholar
Smith, B., Sigurdsson, S., & Abel, T. 2008, MNRAS, 385, 1443CrossRefGoogle Scholar
Smith, B. D. & Sigurdsson, S. 2007, ApJl, 661, L5CrossRefGoogle Scholar
Tumlinson, J. 2007, ApJl, 664, L63CrossRefGoogle Scholar