论文标题
用贝叶斯分层模型评估SN IA锚数据数据集的校准
Evaluating the Calibration of SN Ia Anchor Datasets with a Bayesian Hierarchical Model
论文作者
论文摘要
在宇宙学研究中，使用IA型超新星（SNE IA），调查间校准仍然是重要的系统不确定性。理想情况下，每个调查都将测量其系统吞吐量，例如带有带通测量结果，并结合了表征良好的分光光度计标准恒星的观察；但是，附近的许多重要的SN调查尚未做到这一点。我们通过将其三级调查星与Pan-Starrs1 G，R和I和SDSS/CSP U进行重新校准。通过将每个望远镜/摄像头的空间可变零点考虑并在调查的自然仪器光度法系统中应用改进的颜色转换，这可以改善先前的重新校准工作。我们的分析使用数据的全局分层模型，该模型产生了幅度偏移和带通移的协方差矩阵，从而量化和减少了校准中系统的不确定性。我们称我们的方法为均匀的重新分析（X-Calibur）称为跨校准。这种方法不仅从复杂的分析中获得，而且还可以将我们的校准与更多的颜色校准器联系起来，而不仅仅是以前的努力所做的那样，而不仅仅是一种彩色校准器（BD+17 4708）。此处介绍的结果有可能帮助了解和改善即将进行的SN IA宇宙学分析的校准不确定性。
Inter-survey calibration remains an important systematic uncertainty in cosmological studies using type Ia supernova (SNe Ia). Ideally, each survey would measure its system throughputs, for instance with bandpass measurements combined with observations of well-characterized spectrophotometric standard stars; however, many important nearby-SN surveys have not done this. We recalibrate these surveys by tying their tertiary survey stars to Pan-STARRS1 g, r, and i, and SDSS/CSP u. This improves upon previous recalibration efforts by taking the spatially variable zeropoints of each telescope/camera into account, and applying improved color transformations in the surveys' natural instrumental photometric systems. Our analysis uses a global hierarchical model of the data which produces a covariance matrix of magnitude offsets and bandpass shifts, quantifying and reducing the systematic uncertainties in the calibration. We call our method CROSS-CALIBration with a Uniform Reanalysis (X-CALIBUR). This approach gains not only from a sophisticated analysis, but also from simply tying our calibration to more color calibrators, rather than just the one color calibrator (BD+17 4708) as many previous efforts have done. The results presented here have the potential to help understand and improve calibration uncertainties upcoming SN Ia cosmological analyses.
