NASA’s Kepler, K2, and TESS missions employ simple aperture photometry to derive time-series photometry, where an aperture is estimated for each star, and pixels containing each star are summed to create a single light curve. This method is simple, but in crowded fields, the derived time series can be highly contaminated. The alternate method of fitting a point-spread function (PSF) to the data is able to account for crowding but is computationally expensive. In this paper, we present a new approach to extracting photometry from these time-series missions that fits the PSF directly but makes simplifying assumptions in order to greatly reduce the computation expense. Our method fixes the scene of the field in each image, estimates the PSF shape of the instrument with a linear model, and allows only source flux and position to vary. We demonstrate that our method is able to separate the photometry from blended targets in the Kepler data set that are separated by less than a pixel. Our method is fast to compute and fully accounts for uncertainties from degeneracies due to crowded fields. We name the method described in this work linearized field deblending photometry. We demonstrate our method on the false-positive Kepler target KOI-608. We are able to separate the photometry of the two sources in the data and demonstrate that the contaminating transiting signal is consistent with a small, substellar companion with a radius of 2.67 R _Jup (0.27 R _☉). Our method is equally applicable to extracting photometry from NASA’s TESS mission.

NASA 的开普勒、K2 和 TESS 任务采用简单的孔径光度法来推导时间序列光度法，其中估计每颗恒星的孔径，并将包含每颗恒星的像素相加以创建单个光曲线。这种方法很简单，但是在拥挤的领域中，导出的时间序列可能会被高度污染。将点扩散函数 (PSF) 拟合到数据的替代方法能够解决拥挤问题，但计算成本很高。在本文中，我们提出了一种从这些时间序列任务中提取光度测量的新方法，该方法直接适合 PSF，但简化了假设以大大降低计算费用。我们的方法固定每个图像中的现场场景，用线性模型估计仪器的 PSF 形状，并且只允许源通量和位置发生变化。我们证明了我们的方法能够将光度测量与开普勒数据集中的混合目标分开，这些目标的间隔小于一个像素。我们的方法可以快速计算并充分考虑由于拥挤的场而导致的退化的不确定性。我们将这项工作中描述的方法命名为线性化场去混合光度法。我们在假阳性开普勒目标 KOI-608 上演示了我们的方法。我们能够将数据中两个来源的光度测量分开，并证明污染凌日信号与半径为 2.67 的小型星下伴星一致 我们将这项工作中描述的方法命名为线性化场去混合光度法。我们在假阳性开普勒目标 KOI-608 上演示了我们的方法。我们能够将数据中两个来源的光度测量分开，并证明污染凌日信号与半径为 2.67 的小型星下伴星一致 我们将这项工作中描述的方法命名为线性化场去混合光度法。我们在假阳性开普勒目标 KOI-608 上演示了我们的方法。我们能够将数据中两个来源的光度测量分开，并证明污染凌日信号与半径为 2.67 的小型星下伴星一致R _Jup (0.27 R _☉ )。我们的方法同样适用于从 NASA 的 TESS 任务中提取光度测量。