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 similar to300 arcmin(2) in the area of the Chandra Deep Field South and the Hubble Deep Field North on five separate search epochs (separated by similar to45 day intervals) to a limiting magnitude of F850LP approximate to 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.
The Hubble Higher z supernova search: Supernovae to z ~ 1.6 and constraints on type Ia progenitor models
ROSATI, Piero;
2004
Abstract
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 similar to300 arcmin(2) in the area of the Chandra Deep Field South and the Hubble Deep Field North on five separate search epochs (separated by similar to45 day intervals) to a limiting magnitude of F850LP approximate to 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.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
