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 present four spectra of the Type Ia supernova SN Ia 2006D extending from -7 to +13 days with respect to B-band maximum. The spectra include the strongest signature of unburned material at photospheric velocities observed in a SN Ia to date. The earliest spectrum exhibits C II absorption features below 14,000 km/s, including a distinctive C II lambda 6580 absorption feature. The carbon signatures dissipate as the SN approaches peak brightness. In addition to discussing implications of photospheric-velocity carbon for white dwarf explosion models, we outline some factors that may influence the frequency of its detection before and around peak brightness. Two effects are explored in this regard, including depopulation of the C II optical levels by non-LTE effects, and line-of-sight effects resulting from a clumpy distribution of unburned material with low volume-filling factor.

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.

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 construct a physically parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of Type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an autoencoder that is interpreted probabilistically after training using a normalizing flow. We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multistage training setup alongside our physically parameterized network, we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including the automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an rms of 0.091 ± 0.010 mag, which corresponds to 0.074 ± 0.010 mag if peculiar velocity contributions are removed. Trained models and codes are released at https://github.com/georgestein/suPAErnova.

We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the Hubble Space Telescope-based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300-9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all presubmission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures ≳ 5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12 mag range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ∼3 mmag. With our large number of observations, careful cross-checks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys.