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 science 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 (LSST) 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 61x improvement in prediction bias <Delta z> on PLAsTiCC simulations and 5x 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.

翻译：即将开展的光度巡天将发现数万颗Ia型超新星（SNe Ia），远超现有光谱资源处理能力。为在缺乏光谱信息时最大化这些观测的科学回报，我们必须仅利用光度信息精确提取关键参数（如超新星红移）。我们提出Photo-zSNthesis方法——一种基于卷积神经网络的技术，用于从多波段超新星光变曲线预测完整红移概率分布，并在模拟的斯隆数字巡天（SDSS）与薇拉·C·鲁宾时空遗产巡天（LSST）数据以及实测SDSS超新星样本上进行了验证。结果表明，相较于现有方法在模拟与实测数据上的预测，本方法实现了显著改进，且红移相关偏差极小——此类偏差通常由选择效应（如马姆奎斯特偏差）导致。具体而言，在PLAsTiCC模拟中，本方法的预测偏置<Δz>较广泛使用的光度红移估计器LCFIT+Z提升了61倍；在实测SDSS数据中提升达5倍。该方法生成的概率密度函数约束性强，将最大化光度型Ia超新星样本的宇宙学约束能力。