Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
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DOI：
10.3847/1538-4357/aceafa
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发表时间：
2023-05
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通讯作者：
H. Qu;M. Sako
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作者：
H. Qu;M. Sako
即将到来的光度测量将发现数万颗Ia型超新星（SNe Ia），大大超过了我们光谱资源的能力。为了在没有光谱信息的情况下最大限度地提高这些观测的科学回报，我们必须仅用光度信息准确地提取关键参数，如SN红移。我们提出了Photo-zSNthesis，一种基于卷积神经网络的方法，用于从多波段超新星光变曲线预测全红移概率分布，在模拟的Sloan数字巡天（SDSS）和Vera C. Rubin Legacy Survey of Space and Time数据以及观测到的SDSS SNe。我们在模拟和真实的观测上都显示了对现有方法预测的重大改进，以及最小的红移依赖偏差，这是一个由于选择效应而带来的挑战，例如，Malmquist偏见。具体来说，我们显示了一个61倍的改善预测偏差Δz的PLASTiCC模拟和5倍的改善真实的SDSS数据相比，广泛使用的光度红移估计，LCFIT+Z。用这种方法产生的PDF受到很好的约束，将最大限度地提高光度Ia超新星样品的宇宙学约束能力。
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.