Finding Planets in K2: A New Method of Cleaning the Data
Abstract
We present a new method of removing systematic flux variations from K2 light curves by employing a pixellevel principal component analysis (PCA). This method decomposes the light curves into its principal components (eigenvectors), each with an associated eigenvalue, the value of which is correlated to how much influence the basis vector has on the shape of the light curve. This method assumes that the most influential basis vectors will correspond to the unwanted systematic variations in the light curve produced by K2’s constant motion. We correct the raw light curve by automatically fitting and removing the strongest principal components. The strongest principal components generally correspond to the flux variations that result from the motion of the star in the field of view. Our primary method of calculating the strongest principal components to correct for in the raw light curve estimates the noise by measuring the scatter in the light curve after using an algorithm for SavitsyGolay detrending, which computes the combined photometric precision value (SGCDPP value) used in classic Kepler. We calculate this value after correcting the raw light curve for each element in a list of cumulative sums of principal components so that we have as many noise estimate values as there are principal components. We then take the derivative of the list of SGCDPP values and take the number of principal components that correlates to the point at which the derivative effectively goes to zero. This is the optimal number of principal components to exclude from the refitting of the light curve. We find that a pixellevel PCA is sufficient for cleaning unwanted systematic and natural noise from K2’s light curves. We present preliminary results and a basic comparison to other methods of reducing the noise from the flux variations.
 Publication:

American Astronomical Society Meeting Abstracts #229
 Pub Date:
 January 2017
 Bibcode:
 2017AAS...22914613C