Uniform Recalibration of Common Spectrophotometry Standard Stars onto the CALSPEC System using the SuperNova Integral Field Spectrograph

Abstract

We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the HST-based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300 Å to 9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standardstar spectra taken on photometric nights. As a modern cosmology analysis, all pre-submission 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-magnitude 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 crosschecks, and 14 reference stars, our

The Spectroscopic Classification of Astronomical Transients (SCAT) Survey: Overview, Pipeline Description, Initial Results, and Future Plans

“…SNfactory is dedicated to time-series spectrophotometry of Type Ia supernovae (SNe Ia) for measuring cosmological parameters. To achieve this goal, they have calibrated the SNIFS spectrophotometric response to within 0.3% of the Hubble Space Telescope (HST) CALSPEC system (Rubin et al 2022), the de facto "gold standard" in spectrophotometry (Bohlin et al 2014). SNfactory is able to reach this level of precision by dedicating a significant amount of time and effort to calibrating each aspect of the optical system, including extensive standard-star observations (e.g., Buton et al 2013) and the implementation of the SNIFS Calibration Apparatus (SCALA; Küsters et al 2016;Lombardo et al 2017;Küsters et al 2020).…”

“…Then, the extraction trace and ellipticity are estimated by fitting a wavelength-dependent 2D Gaussian profile to "meta-slices" with widths of 100 Å and 150 Å for the B and R channels, respectively. Then, we construct the normalized radial PSF which is well-described by a Gaussian core and Moffat wings (Buton 2009;Rubin et al 2022); , , , , , , ,…”

“…For example, large and/or highly variable seeing make it difficult to precisely measure the PSF shape parameters because of the trade-offs between the Gaussian core, the Moffat wings, and the background sky flux. No exposure with a seeing of >2″ is considered photometric, as Rubin et al (2022) showed the PSF becomes poorly behaved under these conditions. Other factors that can degrade the quality of the PSF fit include imprecise focusing or telescope jitter caused by wind shake.…”

A Probabilistic Autoencoder for Type Ia Supernova Spectral Time Series

“…The original spectra span the range 3200-10000 Å simultaneously. The spectra from SNIFS were reduced using the SNfactory data reduction pipeline (Bacon et al 2001;Aldering et al 2006;Scalzo et al 2010), flux calibrated following Buton et al (2013); Rubin et al (2022), and host-galaxy subtracted as in Bongard et al (2011). The spectra were corrected for dust in our Galaxy using the dust map from Schlegel et al (1998) and the extinction-color relation from Cardelli et al (1989).…”

“…Peculiar velocity contributions to the redshifts result in an amplitude shift of less than ∼10% for higher-redshift SNe (z > 0.02). Additionally, the spectra have instrumental "gray" offsets of a few percent (e.g., Rubin et al 2022) that also look like an amplitude. These are unique to each spectrum individually and are typically around ∼2%, but the distribution is very non-Gaussian.…”