Turning Gravitationally Lensed Supernovae into Cosmological Probes
Turning Gravitationally Lensed Supernovae into Cosmological Probes
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DOI：
10.3847/1538-4357/ab164a
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
J. Pierel;Steven A. Rodney
中科院分区：
文献类型：
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作者：
J. Pierel;Steven A. Rodney
最近，有两个具有里程碑意义的引力透镜超新星的发现：第一个多重成像SN，“Refsdal”，以及第一个分解成多张图像的Ia型SN，SN iPTF16geu。对这些物体的多个光曲线进行拟合可以测量透镜时间延迟，这是不同图像的到达时间的差异。这些测量提供了透镜模型的精确测试，或对哈勃常数和其他宇宙学参数的限制，这些参数与局部距离阶梯无关。在接下来的十年里，需要精确的时间延迟测量，以便通过LSST和WFIRST等宽视场时间域测量来发现数十到数百个透镜SNE。我们已经开发了一个开源的软件包，用于模拟和测量多幅图像的SNE的时间延迟，包括改进了对微透镜引起的不确定性的表征。使用该软件包，我们描述的模拟表明，与峰值后检测相比，先导图像的峰值前检测能够实现更准确和更精确的时间延迟测量(分别为∼1和∼2天)。我们还得出结论，在没有精确先验的情况下拟合微透镜的效果通常会导致时延测量中的偏差和对数据的过度拟合，但使用高斯过程回归技术足以确定微透镜的不确定度。
Recently, there have been two landmark discoveries of gravitationally lensed supernovae: the first multiply imaged SN, “Refsdal”, and the first Type Ia SN resolved into multiple images, SN iPTF16geu. Fitting the multiple light curves of such objects can deliver measurements of the lensing time delays, which are the difference in arrival times for the separate images. These measurements provide precise tests of lens models or constraints on the Hubble constant and other cosmological parameters that are independent of the local distance ladder. Over the next decade, accurate time delay measurements will be needed for the tens to hundreds of lensed SNe to be found by wide-field time-domain surveys such as LSST and WFIRST. We have developed an open-source software package for simulations and time delay measurements of multiply imaged SNe, including an improved characterization of the uncertainty caused by microlensing. Using this package, we describe simulations that suggest that a before-peak detection of the leading image enables a more accurate and precise time delay measurement (by ∼1 and ∼2 days, respectively), when compared to an after-peak detection. We also conclude that fitting the effects of microlensing without an accurate prior often leads to biases in the time delay measurement and over-fitting to the data, but that employing a Gaussian Process Regression technique is sufficient for determining the uncertainty due to microlensing.