Everything Every Band All at Once I: A Global Morphology Catalog in Abell 2744 based on UNCOVER/MegaScience

Abstract

We present spectrally-resolved structural parameter measurements of 29,608 sources from the legacy lensing field of Abell 2744, quantifying global structures from observed $0.7 μm - 4.8 μm$ and spanning rest-frame UV to NIR at $R\sim15$. These measurements are made on imaging mosaics mainly from the UNCOVER/MegaScience survey, including 20 JWST NIRCam broad and medium bands. We perform single-component Sérsic fitting to these galaxies using \texttt{pysersic}, a Bayesian structural fitting tool, to infer their structural parameters and associated random uncertainties from the posterior distributions. Through various quality evaluation criteria, we infer robust structural parameters among $> 90\%$ of the selected $\rm SNR>10$ sources. For each galaxy with reliable sizes in at least two bands and a high-quality redshift, we fit its observed size as a function of wavelength and infer rest-frame UV, optical, and near-infrared sizes where applicable. By performing injection-recovery tests on simulated galaxy cutouts in selected bands, we establish that our structural parameter measurements achieve fractional error $< 10 -20\%$ above $\rm SNR>10$. With this paper, all raw structural measurements and fitted rest-frame sizes are quality-flagged, cataloged, and released to the community. Finally, we demonstrate that this catalog enables the structural study of galaxies over an unprecedentedly wide parameter space of redshift ($0.3<z<8$), stellar mass ($\rm 10^{7}\, M_{\odot}<M_{*} <10^{11.5}\, M_{\odot}$), and rest-frame optical size ($\rm 100 \,pc<R_{e}<10\,kpc$), after correcting for lensing magnification.

Figures (12)

• Figure 1: Example Sérsic fits of two galaxies in all 20 NIRCam medium or broad bands. The six rows on the top show the original images, best-fitting models, and residuals of a galaxy (ID: 31573) that is well-described by a single 2D Sérsic model (use fit = 2). The six rows on the bottom present the fitting results of a galaxy (ID: 30351) that has non-Sérsic structural features, such as spiral arms, but reliable size measurements (use fit = 1). The black solid contours show the image mask used in structural fitting. These fits are properly flagged and allow us to characterize galaxy structural parameters as functions of wavelength.
• Figure 2: The gray histogram shows the distribution of number of available fits in the SUPER catalog over available bands (left axis). The red or pink curves show the fraction of those fits obtained from either the SVI-MVN or MCMC method as functions of all 20 NIRCam medium or broad bands (right axis). Roughly $60\%$ of these fits are retrieved from full MCMC sampling, which better characterizes the parameter posterior. The total number of available fits is around 10 to 20 thousand in most of the deep broadband images. In $\rm \sim 1\, mag$ shallower medium band images, the total number of successful fits drops to 4 to 7 thousand. Overall, 29,608 unique galaxies have acceptable fits in at least one band by either one of the pysersic methods.
• Figure 3: The difference in magnitude from this work (structural fitting) and the photometry catalog (aperture photometry) as a function of photometry catalog magnitude, for all galaxies in the SUPER_DUPER catalog in all 20 NIRCam medium or broad bands. We also show the population median and $1\sigma$ scatter in magnitude bins as error bars. Based on the median of these distributions, we find no significant systematic differences between the photometry catalog fluxes and pysersic recovered fluxes at $\rm m_{AB} <26$. However, structural fitting reports systematically higher fluxes than aperture photometry at $\rm m_{AB} >26$, especially in the broadband images, where sources to a fainter limit are selected for fitting. These systematic differences in the faint regime are likely driven by the intrinsic difference between the Sérsic profile and the aperture loss correction, in terms of the assumption of the surface brightness curve of growth in the source outskirt.
• Figure 4: Recovery tests in F444W: The Sérsic parameter fractional error (fitted value minus the true value, normalized by the true value) versus true magnitude (first column from the left) or SNR (second column from the left). From top to bottom, each row shows the fractional error on flux, half-light radius, Sérsic index, and ellipticity. The accuracy of recovered Sérsic parameters decreases with source brightness or SNR. In F444W, pysersic retrieves Sérsic parameters within fractional errors of 10% for the majority ($68\%$) of the instances at a given magnitude or SNR, down to $\rm SNR\sim 30$, which approximately corresponds to $\rm m_{F444W} \sim 26$. There is little ($<2\%$) or no systematic offset in the median fractional errors at $\rm SNR>10$ (or $\rm m_{F444W} < 27$). The panels on the right show the histogram of Sérsic parameter residuals normalized by pysersic uncertainties at $\rm SNR>10$, compared to a unit Gaussian distribution. Notably, pysersic uncertainties encapsulate the discrepancy between true and fitted values exceptionally well for ellipticity. However, pysersic systematically underestimates the uncertainty for flux, half-light radius, and Sérsic index by a factor of roughly 2 (or 1.5, in the case of Sérsic index), as indicated by broadening of these distributions relative to the unit Gaussian distribution.
• Figure 5: The corner plot showing the scatter of fractional errors in flux, half-light radius, and Sérsic index. For any mock instance where the flux is overestimated, its half-light radius (or Sérsic index) is more likely to be overestimated, and vice versa.