We present a new method employing machine-learning techniques for measuring astrophysical features by correcting systematics in IRAC high-precision photometry using random forests. The main systematic in IRAC light-curve data is position changes due to unavoidable telescope motions coupled with an intrapixel response function. We aim to use the large amount of publicly available calibration data for the single pixel used for this type of work (the sweet-spot pixel) to make a fast, easy-to-use, accurate correction to science data. This correction on calibration data has the advantage of using an independent data set instead of the science data themselves, which has the disadvantage of including astrophysical variations. After focusing on feature engineering and hyperparameter optimization, we show that a boosted random forest model can reduce the data such that we measure the median of 10 archival eclipse observations of XO-3b to be 1459 ± 200 ppm. This is a comparable depth to t...

中文翻译：

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Spitzer IRAC将随机森林应用于高精度光度分析

我们提出了一种新的方法，该方法采用机器学习技术通过校正使用随机森林的IRAC高精度测光系统来测量天体物理特征。IRAC光曲线数据中的主要系统数据是由于不可避免的望远镜运动以及像素内响应函数而导致的位置变化。我们旨在将大量公开可用的校准数据用于此类工作所用的单个像素（最佳点像素），以对科学数据进行快速，易于使用的准确校正。校准数据的这种校正具有使用独立数据集而不是科学数据本身的优点，这具有包括天体物理变化的缺点。在专注于特征工程和超参数优化之后，我们显示出增强的随机森林模型可以减少数据，因此我们将XO-3b的10次日食观测的中位数测量为1459±200 ppm。这是可比的深度。