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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 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 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.

The Type Ia supernova (SN Ia) LSQ14fmg exhibits exaggerated properties that may help to reveal the origin of the "super-Chandrasekhar"(or 03fg-like) group. The optical spectrum is typical of a 03fg-like SN Ia, but the light curves are unlike those of any SNe Ia observed. The light curves of LSQ14fmg rise extremely slowly. At -23 rest-frame days relative to B-band maximum, LSQ14fmg is already brighter than MV = -19 mag before host extinction correction. The observed color curves show a flat evolution from the earliest observation to approximately 1 week after maximum. The near-infrared light curves peak brighter than -20.5 mag in the J and H bands, far more luminous than any 03fg-like SNe Ia with near-infrared observations. At 1 month past maximum, the optical light curves decline rapidly. The early, slow rise and flat color evolution are interpreted to result from an additional excess flux from a power source other than the radioactive decay of the synthesized 56Ni. The excess flux matches the interaction with a typical superwind of an asymptotic giant branch (AGB) star in density structure, mass-loss rate, and duration. The rapid decline starting at around 1 month past B-band maximum may be an indication of rapid cooling by active carbon monoxide (CO) formation, which requires a low-temperature and high-density environment. These peculiarities point to an AGB progenitor near the end of its evolution and the core degenerate scenario as the likely explosion mechanism for LSQ14fmg.

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.

We present detailed ultraviolet, optical, and near-infrared light curves of the Type Ia supernova (SN) 2012fr, which exploded in the Fornax cluster member NGC 1365. These precise high-cadence light curves provide a dense coverage of the flux evolution from -12 to +140 days with respect to the epoch of B-band maximum (). Supplementary imaging at the earliest epochs reveals an initial slow and nearly linear rise in luminosity with a duration of ∼2.5 days, followed by a faster rising phase that is well reproduced by an explosion model with a moderate amount of 56Ni mixing in the ejecta. From our analysis of the light curves, we conclude that: (i) the explosion occurred 1800) luminosity was 16.5 ±0.6 days, (iii) the supernova suffered little or no host-galaxy dust reddening, (iv) the peak luminosity in both the optical and near-infrared was consistent with the bright end of normal Type Ia diversity, and (v) 0.60 ±0.15 M o of 56Ni was synthesized in the explosion. Despite its normal luminosity, SN 2012fr displayed unusually prevalent high-velocity Ca ii and Si ii absorption features, and a nearly constant photospheric velocity of the Si ii λ6355 line at ∼12,000 that began ∼5 days before . We also highlight some of the other peculiarities in the early phase photometry and the spectral evolution. SN 2012fr also adds to a growing number of Type Ia supernovae that are hosted by galaxies with direct Cepheid distance measurements.

Correcting Type Ia Supernova brightnesses for extinction by dust has proven to be a vexing problem. Here we study the dust foreground to the highly reddened SN 2012cu, which is projected onto a dust lane in the galaxy NGC 4772. The analysis is based on multi-epoch, spectrophotometric observations spanning from 3300-9200 Å, obtained by the Nearby Supernova Factory. Phase-matched comparison of the spectroscopically twinned SN 2012cu and SN 2011fe across 10 epochs results in the best-fit color excess of (E (B - V), RMS) = (1.00, 0.03) and total-to-selective extinction ratio of (RV, RMS) = (2.95, 0.08) toward SN 2012cu within its host galaxy. We further identify several diffuse interstellar bands and compare the 5780 Å band with the dust-to-band ratio for the Milky Way (MW). Overall, we find the foreground dust-extinction properties for SN 2012cu to be consistent with those of the MW. Furthermore, we find no evidence for significant time variation in any of these extinction tracers. We also compare the dust extinction curve models of Cardelli et al., ODonnell, and Fitzpatrick, and find the predictions of Fitzpatrick fit SN 2012cu the best. Finally, the distance to NGC4772, the host of SN 2012cu, at a redshift of z = 0.0035, often assigned to the Virgo Southern Extension, is determined to be 16.6 ± 1.1 Mpc. We compare this result with distance measurements in the literature.

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.

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.