标题： 利用Ia型超新星的 photometric redshifts 评估宇宙学偏差——以暗能量巡天超新星项目为例 显示英文标题

标题： Evaluating Cosmological Biases using Photometric Redshifts for Type Ia Supernova Cosmology with the Dark Energy Survey Supernova Program

摘要： 基于Ia型超新星（SNe Ia）的宇宙学分析传统上依赖于光谱学来分类超新星类型并获得可靠的红移以测量距离-红移关系。 虽然对于大多数超新星获得宿主星系的光谱红移对小范围瞬态巡天来说是可行的，但对于即将开展的大范围巡天（如Vera Rubin天文台的空间与时间遗产巡天）来说，这将过于耗费资源，该巡天预计会观测到数百万个超新星。 在这里，我们利用暗能量巡天（DES）的数据，通过直接从超新星光变曲线推断出的 photometric redshift（photo-z），结合来自宿主星系 photo-z 估计的高斯和完整 p(z) 先验，来解决这个问题。 使用DES五年期的 photometrically-classified SN 样本，我们考虑了几种作为宿主星系 photo-z 先验的 photo-z 算法，包括 Self-Organizing Map redshifts（SOMPZ）、Bayesian Photometric Redshifts（BPZ）以及 DES 的 3x2 点分析中使用的 Directional-Neighbourhood Fitting（DNF）redshift 估计。 通过详细的DES五年期样本目录级模拟，我们发现当使用 SN+SOMPZ 或 DNF 先验 photo-z 时，模拟的 w 可以在$\pm$0.02 范围内恢复，小于这些样本的平均统计不确定性 0.03。 通过对数据的分析，我们在五种 photo-z 变体中的三种中获得了与模拟一致的 w 偏差，偏差约为 ~1$\sigma$。 我们进一步评估了 photo-z 系统性误差与 photometric classification 的相互作用，并发现分类引入了一个次主导的系统性成分。 这项工作为下一代完全基于 photometric 的 SNe Ia 宇宙学分析奠定了基础。 显示英文摘要

摘要： Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance-redshift relation. While obtaining a host-galaxy spectroscopic redshift for most SNe is feasible for small-area transient surveys, it will be too resource intensive for upcoming large-area surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time, which will observe on the order of millions of SNe. Here we use data from the Dark Energy Survey (DES) to address this problem with photometric redshifts (photo-z) inferred directly from the SN light-curve in combination with Gaussian and full p(z) priors from host-galaxy photo-z estimates. Using the DES 5-year photometrically-classified SN sample, we consider several photo-z algorithms as host-galaxy photo-z priors, including the Self-Organizing Map redshifts (SOMPZ), Bayesian Photometric Redshifts (BPZ), and Directional-Neighbourhood Fitting (DNF) redshift estimates employed in the DES 3x2 point analyses. With detailed catalog-level simulations of the DES 5-year sample, we find that the simulated w can be recovered within $\pm$0.02 when using SN+SOMPZ or DNF prior photo-z, smaller than the average statistical uncertainty for these samples of 0.03. With data, we obtain biases in w consistent with simulations within ~1$\sigma$ for three of the five photo-z variants. We further evaluate how photo-z systematics interplay with photometric classification and find classification introduces a subdominant systematic component. This work lays the foundation for next-generation fully photometric SNe Ia cosmological analyses.