Hubble Residual Correlations with Spectroscopic Host Properties from the SDSS-II Supernova Survey

Details Hubble Residual Correlations with Spectroscopic Host Properties from the SDSS-II Supernova Survey Hubble Residual Correlations with Spectroscopic Host Properties from the SDSS-II Supernova Survey

Jan 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012aas...21912605d&link_type=abstract

American Astronomical Society, AAS Meeting #219, #126.05

Statistics

Scientific paper

It is well known that the most luminous Type Ia supernovae (SNe Ia) occur preferentially in galaxies with low mass and high star-formation rates. This is an important effect for SN cosmology only if the standard SNe Ia luminosity correlation with light-curve shape and color are affected by differences in environment. In the past few years several groups have shown that environment does appear to matter; that residuals on the Hubble Diagram are correlated with host galaxy mass, metallicity, and star-formation rate. We study this effect in a new way with SNe Ia discovered as part of the SDSS-II Supernova Survey. In contrast to previous works that use photometric estimates of host mass as a proxy for global metallicity, we analyze spectra of emission-line host galaxies to obtain gas-phase metallicities and star-formation rates. We restrict our analysis to SNe at redshifts z < 0.15, where the selection effects of the survey are known to yield a complete SNe Ia sample. We also minimize the bias in our sample with respect to measured host-galaxy properties by obtaining spectra for nearly all hosts, spanning a range in absolute magnitude of -23 < Mr < -17. From a final sample of 33 galaxies, we find that light-curve corrected SNe Ia are 0.11 magnitudes brighter in the mean in high-metallicity hosts than in low-metallicity hosts. We also find a significant correlation between the Hubble residuals of SNe Ia and the specific star-formation rate of the host galaxy. As the statistics of well-observed SNe Ia at high redshift increases, the evolution of the typical environment with respect to the local sample could become a key source of systematic bias.

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