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 int
rinsic 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 A. 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 an be applied to standardization of SNe Ia for cosmology.
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 $0.101 pm 0.007$ mag, which corresponds to $0.084 pm 0.009$ mag if peculiar velocity contributions are removed and $0.073 pm 0.008$ 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 $0.229 pm 0.045$ mag. Standardization using the Twins Embedding also significantly decreases host-galaxy correlations. We recover a host mass step of $0.040 pm 0.020$ mag compared to $0.092 pm 0.024$ 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 have discovered an anomalous behavior of CCD readout electronics that affects their use in many astronomical applications. An offset in the digitization of the CCD output voltage that depends on the binary encoding of one pixel is added to pixels
that are read out one, two and/or three pixels later. One result of this effect is the introduction of a differential offset in the background when comparing regions with and without flux from science targets. Conventional data reduction methods do not correct for this offset. We find this effect in 16 of 22 instruments investigated, covering a variety of telescopes and many different front-end electronics systems. The affected instruments include LRIS and DEIMOS on the Keck telescopes, WFC3-UVIS and STIS on HST, MegaCam on CFHT, SNIFS on the UH88 telescope, GMOS on the Gemini telescopes, HSC on Subaru, and FORS on VLT. The amplitude of the introduced offset is up to 4.5 ADU per pixel, and it is not directly proportional to the measured ADU level. We have developed a model that can be used to detect this binary offset effect in data and correct for it. Understanding how data are affected and applying a correction for the effect is essential for precise astronomical measurements.
We introduce a method for identifying twin Type Ia supernovae, and using them to improve distance measurements. This novel approach to Type Ia supernova standardization is made possible by spectrophotometric time series observations from the Nearby S
upernova Factory (SNfactory). We begin with a well-measured set of supernovae, find pairs 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 that pairs of supernovae with more closely matched spectra indeed have reduced brightness dispersion. We are able to standardize this initial set of SNfactory supernovae to 0.083 +/- 0.012 magnitudes, implying a dispersion of 0.072 +/- 0.010 magnitudes in the absence of peculiar velocities. We estimate that with larger numbers of comparison SNe, e.g, using the final SNfactory spectrophotometric dataset as a reference, this method will be capable of standardizing high-redshift supernovae to within 0.06-0.07 magnitudes. These results imply that at least 3/4 of the variance in Hubble residuals in current supernova cosmology analyses is due to previously unaccounted-for astrophysical differences among the supernovae
