We use the Sloan Digital Sky Survey II Supernova Survey (SDSS-II SNS) data to measure the volumetric core collapse supernova (CCSN) rate in the redshift range (0.03<z<0.09). Using a sample of 89 CCSN we find a volume-averaged rate of (1.06 +/- 0.19)
x 10**(-4)/(yr Mpc**3) at a mean redshift of 0.072 +/- 0.009. We measure the CCSN luminosity function from the data and consider the implications on the star formation history.
We present the dependences of the properties of type Ia Supernovae (SNe Ia) on their host galaxies by analyzing the multi-band lightcurves of 118 spectroscopically confirmed SNe Ia observed by the Sloan Digital Sky Survey (SDSS) Supernova Survey and
the spectra of their host galaxies. We derive the equivalent width of the rm{H}$alpha$ emission line, star formation rate, and gas-phase metallicity from the spectra and compare these with the lightcurve widths and colors of SNe Ia. In addition, we compare host properties with the deviation of the observed distance modulus corrected for lightcurve parameters from the distance modulus determined by the best fit cosmological parameters. This allows us to investigate uncorrected systematic effects in the magnitude standardization. We find that SNe Ia in host galaxies with a higher star formation rate have synthesized on average a larger $^{56}$Ni mass and show wider lightcurves. The $^{56}$Ni mass dependence on metallicity is consistent with a prediction of Timmes et al. 2003 based on nucleosynthesis. SNe Ia in metal-rich galaxies ({$log_{10}(O/H)+12>8.9$) have become 0.13 $pm$ 0.06 magnitude brighter after corrections for their lightcurve widths and colors, which corresponds to up to 6% uncertainty in the luminosity distance. We investigate whether parameters for standardizing SN Ia maximum magnitude differ among samples with different host characteristics. The coefficient of the color term is larger by 0.67 $pm$ 0.19 for SNe Ia in metal-poor hosts than those in metal-rich hosts when no color cuts are imposed.
Large planned photometric surveys will discover hundreds of thousands of supernovae (SNe), outstripping the resources available for spectroscopic follow-up and necessitating the development of purely photometric methods to exploit these events for co
smological study. We present a light-curve fitting technique for SN Ia photometric redshift (photo-z) estimation in which the redshift is determined simultaneously with the other fit parameters. We implement this LCFIT+Z technique within the frameworks of the MLCS2k2 and SALT-II light-curve fit methods and determine the precision on the redshift and distance modulus. This method is applied to a spectroscopically confirmed sample of 296 SNe Ia from the SDSS-II Supernova Survey and 37 publicly available SNe Ia from the Supernova Legacy Survey (SNLS). We have also applied the method to a large suite of realistic simulated light curves for existing and planned surveys, including SDSS, SNLS, and LSST. When intrinsic SN color fluctuations are included, the photo-z precision for the simulation is consistent with that in the data. Finally, we compare the LCFIT+Z photo-z precision with previous results using color-based SN photo-z estimates.
We describe a general analysis package for supernova (SN) light curves, called SNANA, that contains a simulation, light curve fitter, and cosmology fitter. The software is designed with the primary goal of using SNe Ia as distance indicators for the
determination of cosmological parameters, but it can also be used to study efficiencies for analyses of SN rates, estimate contamination from non-Ia SNe, and optimize future surveys. Several SN models are available within the same software architecture, allowing technical features such as K-corrections to be consistently used among multiple models, and thus making it easier to make detailed comparisons between models. New and improved light-curve models can be easily added. The software works with arbitrary surveys and telescopes and has already been used by several collaborations, leading to more robust and easy-to-use code. This software is not intended as a final product release, but rather it is designed to undergo continual improvements from the community as more is learned about SNe. Below we give an overview of the SNANA capabilities, as well as some of its limitations. Interested users can find software downloads and more detailed information from the manuals at http://www.sdss.org/supernova/SNANA.html .
We present measurements of the Hubble diagram for 103 Type Ia supernovae (SNe) with redshifts 0.04 < z < 0.42, discovered during the first season (Fall 2005) of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey. These data fill in the redshi
ft desert between low- and high-redshift SN Ia surveys. We combine the SDSS-II measurements with new distance estimates for published SN data from the ESSENCE survey, the Supernova Legacy Survey, the Hubble Space Telescope, and a compilation of nearby SN Ia measurements. Combining the SN Hubble diagram with measurements of Baryon Acoustic Oscillations from the SDSS Luminous Red Galaxy sample and with CMB temperature anisotropy measurements from WMAP, we estimate the cosmological parameters w and Omega_M, assuming a spatially flat cosmological model (FwCDM) with constant dark energy equation of state parameter, w. For the FwCDM model and the combined sample of 288 SNe Ia, we find w = -0.76 +- 0.07(stat) +- 0.11(syst), Omega_M = 0.306 +- 0.019(stat) +- 0.023(syst) using MLCS2k2 and w = -0.96 +- 0.06(stat) +- 0.12(syst), Omega_M = 0.265 +- 0.016(stat) +- 0.025(syst) using the SALT-II fitter. We trace the discrepancy between these results to a difference in the rest-frame UV model combined with a different luminosity correction from color variations; these differences mostly affect the distance estimates for the SNLS and HST supernovae. We present detailed discussions of systematic errors for both light-curve methods and find that they both show data-model discrepancies in rest-frame $U$-band. For the SALT-II approach, we also see strong evidence for redshift-dependence of the color-luminosity parameter (beta). Restricting the analysis to the 136 SNe Ia in the Nearby+SDSS-II samples, we find much better agreement between the two analysis methods but with larger uncertainties.
