Star-Image Centering with Deep Learning II: HST/WFPC2 Full Field of View

TL;DR

This paper enhances a deep learning method for precise star image centering on HST/WFPC2 images by accounting for PSF variation and nonlinear effects, significantly reducing pixel-phase bias and improving accuracy across the full detector field.

Contribution

The study introduces a global position and magnitude-aware deep learning model for star centering, extending previous work limited to the detector's central region.

Findings

Eliminates pixel-phase bias in star centering.

Achieves 8-10 mpix uncertainty across the full field.

Corrects PSF variation effects of about 100 mpix.

Abstract

We present an expanded and improved deep-learning (DL) methodology for determining centers of star images on HST/WFPC2 exposures. Previously, we demonstrated that our DL model can eliminate the pixel-phase bias otherwise present in these undersampled images; however that analysis was limited to the central portion of each detector. In the current work we introduce the inclusion of global positions to account for the PSF variation across the entire chip and instrumental magnitudes to account for nonlinear effects such as charge transfer efficiency. The DL model is trained using a unique series of WFPC2 observations of globular cluster 47 Tuc, data sets comprising over 600 dithered exposures taken in each of two filters, F555W and F814W. It is found that the PSF variations across each chip correspond to corrections of the order of 100 mpix, while magnitude effects are at a level of about 10 mpix. Importantly, pixel-phase bias is eliminated with the DL model; whereas, with a classic centering algorithm, the amplitude of this bias can be up to 40 mpix. Our improved DL model yields star-image centers with uncertainties of 8-10 mpix across the full field of view of WFPC2.