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. The PDFs produced by this method are well-constrained and will maximize the cosmological constraining power of photometric SNe Ia samples.

翻译：即将开展的光学巡天将发现数以万计的Ia型超新星（SNe Ia），其数量远超现有光谱资源的处理能力。为在没有光谱信息的情况下最大化这些观测数据的科学回报，我们需仅利用测光信息精确提取诸如超新星红移等关键参数。本文提出Photo-zSNthesis方法——一种基于卷积神经网络的测光超新星红移概率分布预测方法，该方法可从多波段超新星光变曲线中预测完整的红移概率分布。我们在模拟的斯隆数字巡天（SDSS）与维拉·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，本方法在模拟数据与实测数据上均取得显著改进，且红移相关偏差极小——该偏差因马奎斯特偏差等选择效应而难以克服。该方法产生的概率密度函数具有良好的约束性，将最大化测光Ia型超新星样本的宇宙学约束能力。