Star-Image Centering with Deep Learning II: HST/WFPC2 Full Field of View
arxiv(2024)
摘要
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.
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