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  • We present an expanded and improved deep-learning (DL) methodology for determining centers of star images on Hubble Space Telescope/Wide-Field Planetary Camera 2 (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 point-spread function (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 ∼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. © 2024. The Astronomical Society of the Pacific. All rights reserved.

  • Symbolic regression techniques are promising approaches to learning mathematical models that fit experimental data. One of the most powerful techniques for symbolic regression is Grammatical Evolution (GE). This evolutionary computation technique explores a space of candidate models that are ensured to be syntactically correct expressions built from a set of arbitrary building blocks and operators. In GE the syntax for these expressions is defined by a problem-specific formal grammar. Therefore, GE can produce an explainable solution (e.g. a formula), not a black-box model. The current contribution assesses the viability of GE for PSF characterization, using real datasets from HST/WFPC2. Our experiments show that our method is able to find the most likely candidate mathematical expression for the PSF shape, and can also model combinations of shapes taken from a predefined family of functions commonly used in astronomy (Gaussian and Moffat PSFs). These results support the hypothesis that the expressive power of GE can be used to tackle the problem of characterization of complex PSF functions, for example, as a necessary step in the prediction of intra-pixel position of stars. © 2024 SPIE.

Last update from database: 3/13/26, 4:15 PM (UTC)

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