标题： TRAP：一种用于在小角距离下改进系外行星直接探测的时间系统模型 显示英文标题

标题： TRAP: A temporal systematics model for improved direct detection of exoplanets at small angular separations

摘要： 高对比成像巡天在系外行星探测中显示，在大分离度处的巨行星是罕见的。 推动到更小的分离度进行探测是很重要的，因为参数空间的这一部分包含大多数行星。 传统方法在对瞳孔稳定观测进行后处理时，性能在较小的分离度下会下降，这是由于需要更大的场旋转来将源从探测器上移开，再加上更高程度的恒星污染的固有难度。 我们开发了一种提取系外行星信号的方法，该方法在小角分离度下提高了性能。 可以使用不同位置的参考像素创建每个像素的系统性时间行为的数据驱动模型，假设系统性的根本原因在多个像素之间是共享的。 这在高对比成像中的斑点图案中大部分是正确的。 在我们的因果回归模型中，我们同时拟合一个“穿越”探测器像素的行星信号模型和描述斑点图案共享时间趋势的非局部参考光变曲线，以找到最佳拟合的时间模型来描述信号。 通过我们实现的空间非局部、时间系统性模型，称为TRAP，我们展示了与基于时间位移图像之间空间相关性的模型相比，在近距离（$<3\lambda/D$）下对比度可以提高多达6倍。 我们展示了更好的时间采样可以显著提高对比度。 对于$\beta$Pic数据的短积分时间，我们比空间系统性模型将行星的信噪比提高了4倍。 最后，我们展示了时间模型可以用于未对齐的数据，这些数据仅进行了暗电流和平场校正，而不需要进一步的预处理。 显示英文摘要

摘要： High-contrast imaging surveys for exoplanet detection have shown giant planets at large separations to be rare. It is important to push towards detections at smaller separations, the part of the parameter space containing most planets. The performance of traditional methods for post-processing of pupil-stabilized observations decreases at smaller separations, due to the larger field-rotation required to displace a source on the detector in addition to the intrinsic difficulty of higher stellar contamination. We developed a method of extracting exoplanet signals that improves performance at small angular separations. A data-driven model of the temporal behavior of the systematics for each pixel can be created using reference pixels at a different position, assuming the underlying causes of the systematics are shared across multiple pixels. This is mostly true for the speckle pattern in high-contrast imaging. In our causal regression model, we simultaneously fit the model of a planet signal "transiting" over detector pixels and non-local reference lightcurves describing a basis of shared temporal trends of the speckle pattern to find the best fitting temporal model describing the signal. With our implementation of a spatially non-local, temporal systematics model, called TRAP, we show that it is possible to gain up to a factor of 6 in contrast at close separations ($<3\lambda/D$) compared to a model based on spatial correlations between images displaced in time. We show that better temporal sampling resulting in significantly better contrasts. At short integration times for $\beta$ Pic data, we increase the SNR of the planet by a factor of 4 compared to the spatial systematics model. Finally, we show that the temporal model can be used on unaligned data which has only been dark and flat corrected, without the need for further pre-processing.