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 introduce a method for identifying "twin" Type Ia supernovae (SNe Ia) and using them to improve distance measurements. This novel approach to SN Ia standardization is madeossible by spectrophotometric time series observations from the Nearby Supernova Factory (SNfactory). We begin with a well-measured set of SNe, findairs whose spectra match well across the entire optical window, and then test whether this leads to a smaller dispersion in their absolute brightnesses. This analysis is completed in a blinded fashion, ensuring that decisions made in implementing the method do not inadvertently bias the result. We find thatairs of SNe with more closely matched spectra indeed have reduced brightness dispersion. We are able to standardize this initial set of SNfactory SNe to 0.083 0.012 mag, implying a dispersion of 0.072 0.010 mag in the absence ofeculiar velocities. We estimate that with larger numbers of comparison SNe, e.g., using the final SNfactory spectrophotometric data set as a reference, this method will be capable of standardizing high-redshift SNe to within 0.06-0.07 mag. These results imply that at least 3/4 of the variance in Hubble residuals in current SN cosmology analyses is due toreviously unaccounted-for astrophysical differences among the SNe.

Context. The Public European Southern Observatory Spectroscopic Survey of Transient Objects (PESSTO) began as a public spectroscopic survey in April 2012. PESSTO classifies transients from publicly available sources and wide-field surveys, and selects science targets for detailed spectroscopic and photometric follow-up. PESSTO runs for nine months of the year, January - April and August - December inclusive, and typically has allocations of 10 nights per month. Aims. We describe the data reduction strategy and data products that are publicly available through the ESO archive as the Spectroscopic Survey data release 1 (SSDR1). Methods. PESSTO uses the New Technology Telescope with the instruments EFOSC2 and SOFI to provide optical and NIR spectroscopy and imaging. We target supernovae and optical transients brighter than 20.5m for classification. Science targets are selected for follow-up based on the PESSTO science goal of extending knowledge of the extremes of the supernova population. We use standard EFOSC2 set-ups providing spectra with resolutions of 13-18 Å between 3345-9995 Å. A subset of the brighter science targets are selected for SOFI spectroscopy with the blue and red grisms (0.935-2.53 μm and resolutions 23-33 Å) and imaging with broadband JHKs filters. Results. This first data release (SSDR1) contains flux calibrated spectra from the first year (April 2012-2013). A total of 221 confirmed supernovae were classified, and we released calibrated optical spectra and classifications publicly within 24 h of the data being taken (via WISeREP). The data in SSDR1 replace those released spectra. They have more reliable and quantifiable flux calibrations, correction for telluric absorption, and are made available in standard ESO Phase 3 formats. We estimate the absolute accuracy of the flux calibrations for EFOSC2 across the whole survey in SSDR1 to be typically ∼15%, although a number of spectra will have less reliable absolute flux calibration because of weather and slit losses. Acquisition images for each spectrum are available which, in principle, can allow the user to refine the absolute flux calibration. The standard NIR reduction process does not produce high accuracy absolute spectrophotometry but synthetic photometry with accompanying JHKs imaging can improve this. Whenever possible, reduced SOFI images are provided to allow this. Conclusions. Future data releases will focus on improving the automated flux calibration of the data products. The rapid turnaround between discovery and classification and access to reliable pipeline processed data products has allowed early science papers in the first few months of the survey.