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Abstract Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the scientific return of these observations in the absence of spectroscopic information, we must accurately extract key parameters, such as SN redshifts, with photometric information alone. We present Photo-zSNthesis, a convolutional neural network-based method for predicting full redshift probability distributions from multi-band supernova lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera C. Rubin Legacy Survey of Space and Time data as well as observed SDSS SNe. We show major improvements over predictions from existing methods on both simulations and real observations as well as minimal redshift-dependent bias, which is a challenge due to selection effects, e.g., Malmquist bias. Specifically, we show a 61× improvement in prediction bias 〈Δz〉 on PLAsTiCC simulations and 5× improvement on real SDSS data compared to results from a widely used photometric redshift estimator, LCFIT+Z. The PDFs produced by this method are well constrained and will maximize the cosmological constraining power of photometric SNe Ia samples.
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Astrometric Redshifts of Supernovae
Abstract Differential Chromatic Refraction (DCR) is caused by the wavelength dependence of our atmosphere’s refractive index, which shifts the apparent positions of stars and galaxies and distorts their shapes depending on their spectral energy distributions. While this effect is typically mitigated and corrected for in imaging observations, we investigate how DCR can instead be used to our advantage to infer the redshifts of supernovae from multiband, time-series imaging data. We simulate Type Ia supernovae in the proposed Vera C. Rubin Observatory Legacy Survey of Space and Time Deep Drilling Field, and evaluate astrometric redshifts. We find that the redshift accuracy improves dramatically with the statistical quality of the astrometric measurements as well as with the accuracy of the astrometric solution. For a conservative choice of a 5 mas systematic uncertainty floor, we find that our redshift estimation is accurate atz< 0.6. We then combine our astrometric redshifts with both host-galaxy photometric redshifts and supernovae photometric (light-curve) redshifts and show that this considerably improves the overall redshift estimates. These astrometric redshifts will be valuable, especially since Rubin will discover a vast number of supernovae for which we will not be able to obtain spectroscopic redshifts.
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- Award ID(s):
- 2108094
- PAR ID:
- 10559858
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 977
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 199
- Size(s):
- Article No. 199
- Sponsoring Org:
- National Science Foundation
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