Random Forests Applied to High-precision Photometry Analysis with Spitzer IRAC

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 the average of those in the literature done by seven different methods; however, the spread in measurements is 30%-100% larger than those literature values, depending on the reduction method. We also caution others attempting similar methods to check their results with the fiducial data set of XO-3b, as we were also able to find models providing initially great scores on their internal test data sets but whose results significantly underestimated the eclipse depth of that planet.