The use of Type Ia supernovae as distance indicators led to the discovery of the accelerating expansion of the universe a decade ago. Now that large second generation surveys have significantly increased the size and quality of the high-redshift sample, the cosmological constraints are limited by the currently available sample of ~50 cosmologically useful nearby supernovae. The Nearby Supernova Factory addresses this problem by discovering nearby supernovae and observing their spectrophotometric time development. Our data sample includes over 2400 spectra from spectral timeseries of 185 supernovae. This talk presents results from a portion of this sample including a Hubble diagram (relative distance vs. redshift) and a description of some analyses using this rich dataset.

We estimate systematic errors due to K-corrections in standard photometric analyses of high-redshift Type Iasupernovae. Errors due to K-correction occur when the spectral template model underlying the light curve fitterpoorly represents the actual supernova spectral energy distribution, meaning that the distance modulus cannot berecovered accurately. In order to quantify this effect, synthetic photometry is performed on artificially redshiftedspectrophotometric data from 119 low-redshift supernovae from the Nearby Supernova Factory, and the resultinglight curves are fit with a conventional light curve fitter. We measure the variation in the standardized magnitudethat would be fit for a given supernova if located at a range of redshifts and observed with various filter setscorresponding to current and future supernova surveys. We find significant variation in the measurements of thesame supernovae placed at different redshifts regardless of filters used, which causes dispersion greater than?0.05 mag for measurements of photometry using the Sloan-like filters and a bias that corresponds to a 0.03 shift inw when applied to an outside data set. To test the result of a shift in supernova population or environment at higherredshifts, we repeat our calculations with the addition of a reweighting of the supernovae as a function of redshiftand find that this strongly affects the results and would have repercussions for cosmology. We discuss possiblemethods to reduce the contribution of the K-correction bias and uncertainty.

We present a sample of normal Type Ia supernovae (SNe Ia) from the Nearby Supernova Factory data set with spectrophotometry at sufficiently late phases to estimate the ejected mass using the bolometric light curve.Wemeasure 56 Ni masses from the peak bolometric luminosity, then compare the luminosity in the 56Co-decay tail to the expected rate of radioactive energy release from ejecta of a given mass. We infer the ejected mass in a Bayesian context using a semi-analytic model of the ejecta, incorporating constraints from contemporary numerical models as priors on the density structure and distribution of 56Ni throughout the ejecta. We find a strong correlation between ejected mass and light-curve decline rate, and consequently 56Ni mass, with ejected masses in our data ranging from 0.9 to 1.4 M⊙. Most fast-declining (SALT2 x1 < -1) normal SNe Ia have significantly sub-Chandrasekhar ejected masses in our fiducial analysis. © 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.

We apply the color-magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric data set. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the linear region in the color-magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness. We define a new parameter, ω XY , the size of the “bump” feature near maximum brightness for arbitrary filters X and Y. We find a significant correlation between the slope of the linear region, β XY, in the CMAGIC diagram and ω XY. These results may be used to our advantage, as they are less affected by extinction than parameters defined as a function of time. Additionally, ω XY is computed independently of templates. We find that current empirical templates are successful at reproducing the features described in this work, particularly SALT3, which correctly exhibits the negative correlation between slope and “bump” size seen in our data. In 1D simulations, we show that the correlation between the size of the “bump” feature and β XY can be understood as a result of chemical mixing due to large-scale Rayleigh-Taylor instabilities.