Star-image Centering with Deep Learning: HST/WFPC2 Images

Abstract

A deep learning (DL) algorithm is built and tested for its ability to determine centers of star images in HST/WFPC2 exposures, in filters F555W and F814W. These archival observations hold great potential for proper-motion studies, but the undersampling in the camera’s detectors presents challenges for conventional centering algorithms. Two exquisite data sets of over 600 exposures of the cluster NGC 104 in these filters are used as a testbed for training and evaluating the DL code. Results indicate a single-measurement standard error from 8.5 to 11 mpix, depending on the detector and filter. This compares favorably to the ∼20 mpix achieved with the customary “effective point spread function (PSF)” centering procedure for WFPC2 images. Importantly, the pixel-phase error is largely eliminated when using the DL method. The current tests are limited to the central portion of each detector; in future studies, the DL code will be modified to allow for the known variation of the PSF across the detectors.