一个坏主意：测试Spitzer Space Telescope Photometry的PDKEEPAAA标测
Knot a Bad Idea: Testing BLISS Mapping for Spitzer Space Telescope Photometry
摘要：
大部分的凌日系外行星科学都依赖于高精度的测光。目前这一代仪器的灵敏度变化可能大于天体物理信号。对于斯皮策太空望远镜上的红光阵列相机（IRAC），一种流行的处理方法是双线性插值亚像素灵敏度（BLISS）映射。作为马尔可夫链蒙特卡罗（MCMC）的一部分，BLISS映射估计像素上许多位置（节点）的灵敏度，然后插值到目标恒星的质心。我们表明，这种嵌入式优化方案可以失配或偏置参数。因此，我们建立了一个$\textit{Spitzer}$食光曲线模型来检验BLISS映射的准确性和精度。我们比较标准的BLISS映射的一个变种，其中结是适合在MCMC，以及多项式模型。这两种类型的BLISS映射给出了类似的日食深度，我们发现，标准结的行为像真正的参数。因此，标准BLISS映射是MCMC中拟合节点的合理捷径。当光子噪声较低时，BLISS贴图变得不准确，但通常很好地近似真实灵敏度。我们还发现，没有一个完美的方法来选择理想的BLISS结的数量使用给定的数据。BLISS映射给出的拟合通常比精确度更精确（即它们过于保守），并且对于重要的日食或具有更多变化灵敏度的像素，该例程比多项式模型更精确。BLISS映射对大多数这些特定的合成数据具有更好的预测能力，这取决于如何处理时间相关的残差。总体而言，我们得出结论，BLISS映射可以是一个合理的灵敏度模型IRAC测光。
英文摘要：
Much of transiting exoplanet science relies on high-precision photometry. The current generation of instruments can exhibit sensitivity variations greater than the astrophysical signals. For the InfraRed Array Camera (IRAC) on the Spitzer Space Telescope, a popular way to handle this is BiLinearly-Interpolated Subpixel Sensitivity (BLISS) mapping. As part of a Markov Chain Monte Carlo (MCMC), BLISS mapping estimates the sensitivity at many locations (knots) on the pixel, then interpolates to the target star's centroids. We show that such embedded optimization schemes can misfit or bias parameters. Thus, we construct a model of $\textit{Spitzer}$ eclipse light curves to test the accuracy and precision of BLISS mapping. We compare standard BLISS mapping to a variant where the knots are fit during the MCMC, as well as to a polynomial model. Both types of BLISS mapping give similar eclipse depths, and we find that standard knots behave like real parameters. Standard BLISS mapping is therefore a reasonable shortcut to fitting for knots in an MCMC. BLISS maps become inaccurate when the photon noise is low, but typically approximate the real sensitivity well. We also find there is no perfect method for choosing the ideal number of BLISS knots to use on given data. BLISS mapping gives fits that are usually more accurate than precise (i.e. they are overly conservative), and the routine is more precise than polynomial models for significant eclipses or pixels with more varied sensitivities. BLISS mapping has better predictive power for most of these particular synthetic data, depending on how one treats time-correlated residuals. Overall, we conclude that BLISS mapping can be a reasonable sensitivity model for IRAC photometry.
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