Artigo Acesso aberto Revisado por pares

The Hubble Higher z Supernova Search: Supernovae to z ≈ 1.6 and Constraints on Type Ia Progenitor Models

2004; IOP Publishing; Volume: 613; Issue: 1 Linguagem: Inglês

10.1086/422901

ISSN

1538-4357

Autores

L. Strolger, Adam G. Riess, T. Dahlén, Mario Livio, N. Panagia, P. Challis, J. Tonry, A. V. Filippenko, R. Chornock, Henry C. Ferguson, Anton M. Koekemoer, Bahram Mobasher, Mark Dickinson, Mauro Giavalisco, Stefano Casertano, Richard Hook, Stephane Bondin, B. Leibundgut, M. Nonino, P. Rosati, Hyron Spinrad, Charles C. Steidel, Daniel Stern, P. Garnavich, T. Matheson, Norman A. Grogin, A. E. Hornschemeier, C. Kretchmer, Victoria G. Laidler, Kyoungsoo Lee, Ray A. Lucas, D. F. de Mello, Leonidas A. Moustakas, Swara Ravindranath, Marin Richardson, Edward N. Taylor,

Tópico(s)

Astronomy and Astrophysical Research

Resumo

We present results from the Hubble Higher z Supernova Search, the first space-based open field survey for supernovae (SNe). In cooperation with the Great Observatories Origins Deep Survey, we have used the Hubble Space Telescope with the Advanced Camera for Surveys to cover ~300 arcmin2 in the area of the Chandra Deep Field South and the Hubble Deep Field North on five separate search epochs (separated by ~45 day intervals) to a limiting magnitude of F850LP ≈ 26. These deep observations have allowed us to discover 42 SNe in the redshift range 0.2 < z < 1.6. As these data span a large range in redshift, they are ideal for testing the validity of Type Ia supernova progenitor models with the distribution of expected "delay times," from progenitor star formation to Type Ia SN explosion, and the SN rates these models predict. Through a Bayesian maximum likelihood test, we determine which delay-time models best reproduce the redshift distribution of SNe Ia discovered in this survey. We find that models that require a large fraction of "prompt" (less than 2 Gyr) SNe Ia poorly reproduce the observed redshift distribution and are rejected at greater than 95% confidence. We find that Gaussian models best fit the observed data for mean delay times in the range of 2-4 Gyr.

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