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 show how spectra of Type Ia supernovae (SNe Ia) at maximum light can be used to improve cosmological distance estimates. In a companion article, we used manifold learning to build a three-dimensional parameterization of the intrinsic diversity of SNe Ia at maximum light that we call the "Twins Embedding."In this article, we discuss how the Twins Embedding can be used to improve the standardization of SNe Ia. With a single spectrophotometrically calibrated spectrum near maximum light, we can standardize our sample of SNe Ia with an rms of 0.101 0.007 mag, which corresponds to 0.084 0.009 mag if peculiar velocity contributions are removed and to 0.073 0.008 mag if a larger reference sample were obtained. Our techniques can standardize the full range of SNe Ia, including those typically labeled as peculiar and often rejected from other analyses. We find that traditional light-curve width + color standardization such as SALT2 is not sufficient. The Twins Embedding identifies a subset of SNe Ia, including, but not limited to, 91T-like SNe Ia whose SALT2 distance estimates are biased by 0.229 0.045 mag. Standardization using the Twins Embedding also significantly decreases host-galaxy correlations. We recover a host mass step of 0.040 0.020 mag compared to 0.092 0.026 mag for SALT2 standardization on the same sample of SNe Ia. These biases in traditional standardization methods could significantly impact future cosmology analyses if not properly taken into account.

We study the spectral diversity of Type Ia supernovae (SNe Ia) at maximum light using high signal-to-noise spectrophotometry of 173 SNe Ia from the Nearby Supernova Factory. We decompose the diversity of these spectra into different extrinsic and intrinsic components, and we construct a nonlinear parameterization of the intrinsic diversity of SNe Ia that preserves pairings of "twin"SNe Ia. We call this parameterization the "Twins Embedding."Our methodology naturally handles highly nonlinear variability in spectra, such as changes in the photosphere expansion velocity, and uses the full spectrum rather than being limited to specific spectral line strengths, ratios, or velocities. We find that the time evolution of SNe Ia near maximum light is remarkably similar, with 84.6% of the variance in common to all SNe Ia. After correcting for brightness and color, the intrinsic variability of SNe Ia is mostly restricted to specific spectral lines, and we find intrinsic dispersions as low as ∼0.02 mag between 6600 and 7200 Å. With a nonlinear three-dimensional model plus one dimension for color, we can explain 89.2% of the intrinsic diversity in our sample of SNe Ia, which includes several different kinds of "peculiar"SNe Ia. A linear model requires seven dimensions to explain a comparable fraction of the intrinsic diversity. We show how a wide range of previously established indicators of diversity in SNe Ia can be recovered from the Twins Embedding. In a companion article, we discuss how these results can be applied to the standardization of SNe Ia for cosmology.

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.

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.

Aims. The scientific yield of current and future optical surveys is increasingly limited by systematic uncertainties in the flux calibration. This is the case for type Ia supernova (SN Ia) cosmology programs, where an improved calibration directly translates into improved cosmological constraints. Current methodology rests on models of stars. Here we aim to obtain flux calibration that is traceable to state-of-the-art detector-based calibration. Methods. We present the SNIFS Calibration Apparatus (SCALA), a color (relative) flux calibration system developed for the SuperNova integral field spectrograph (SNIFS), operating at the University of Hawaii 2.2 m (UH 88) telescope. Results. By comparing the color trend of the illumination generated by SCALA during two commissioning runs, and to previous laboratory measurements, we show that we can determine the light emitted by SCALA with a long-term repeatability better than 1%. We describe the calibration procedure necessary to control for system aging. We present measurements of the SNIFS throughput as estimated by SCALA observations. Conclusions. The SCALA calibration unit is now fully deployed at the UH 88 telescope, and with it color-calibration between 4000 Å and 9000 Å is stable at the percent level over a one-year baseline.

SN Ia cosmology depends on the ability to fit and standardize observations of supernova magnitudes with an empirical model. We present here a series of new models of SN Ia spectral time series that capture a greater amount of supernova diversity than is possible with the models that are currently customary. These are entitled SuperNova Empirical MOdels (SNEMO; https://snfactory.lbl.gov/snemo). The models are constructed using spectrophotometric time series from 172 individual supernovae from the Nearby Supernova Factory, comprising more than 2000 spectra. Using the available observations, Gaussian processes are used to predict a full spectral time series for each supernova. A matrix is constructed from the spectral time series of all the supernovae, and Expectation Maximization Factor Analysis is used to calculate the principal components of the data. K-fold cross-validation then determines the selection of model parameters and accounts for color variation in the data. Based on this process, the final models are trained on supernovae that have been dereddened using the Fitzpatrick and Massa extinction relation. Three final models are presented here: SNEMO2, a two-component model for comparison with current Type Ia models; SNEMO7, a seven-component model chosen for standardizing supernova magnitudes, which results in a total dispersion of 0.100 mag for a validation set of supernovae, of which 0.087 mag is unexplained (a total dispersion of 0.113 mag with an unexplained dispersion of 0.097 mag is found for the total set of training and validation supernovae); and SNEMO15, a comprehensive 15-component model that maximizes the amount of spectral time-series behavior captured.