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Predicting contrast performance for the Gemini Planet Imager

The Gemini Planet Imager (GPI) is a new exoplanet-hunting instrument mounted on the Gemini South telescope in Chile. Exoplanets, or planets orbiting stars other than the sun, are detected by masking out the light of the star at the center of the image, allowing one to detect objects in its neighborhood. Detection of orbiting planets is often inhibited by noise coming from the turbulence in the atmosphere, which allows light to escape the mask. The instrument tries to actively correct for these perturbations but it is still imperfect, making it difficult to detect the faint exoplanets. There are nights when the atmospheric conditions are such that it is impossible to obtain any usable data, so trying to observe then is a waste of valuable telescope time. There currently is no reliable method to predict the performance of the instrument1. A quantitative measure of whether or not an exoplanet is detectable is given by the contrast, or the ratio of the brightness of the exoplanet to that of the star it orbits. The target variable for each image is the threshold for the lowest possible contrast of a planet that could be viewed on a given night. Therefore, we want these values to be as low as possible to allow for viewing of faint exoplanets. We can approach our prediction in two ways. One way is to predict a numerical contrast value using regression and then allow the researchers to decide whether it is worth observing or not. In this case, we decided to use locally weighted linear regression (LWLR) to predict the actual contrast value. The other way is to treat this as a classification problem by predicting if an image will be good or bad on a given night. For this, we used a Support Vector Machine (SVM) algorithm with a Gaussian kernel to solve for a target labeled as either good or bad. In both cases the features are the environmental variables recorded by the telescope for each observation2. It includes variables such as— but not limited to— the star brightness, wind direction and turbulence. The advantage of predicting a numerical contrast value is that it also provides us with a typical performance for the instrument under current conditions. This information can be used to determine if the instrument is doing better or worse than usual in similar conditions. The classification approach might seem less powerful, but it has the advantage of being a simpler problem and therefore may give a more accurate answer. Previously, our collaborators have examined correlations between the contrast and individual features, determining that performance is mainly driven by the star brightness and the adaptive optics wavefront error [1, 2, 3]. However, a prediction approach utilizing all of the features in a multi-dimensional problem has not been implemented or studied. In Section II, we describe our data set and preprocessing steps. Then in Sections III and IV, we describe our prediction methods and results for a classification and regression problem, respectively. Finally, in Section V, we 1There exist indicators of the current observing conditions but they are not trusted for GPI. Indeed it is not clear what drives GPI performance because GPI utilizes active correction of the turbulence. 2The variables are saved as metadata in each image.