JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: Catalogs of inferred morphological properties of galaxies from JWST/NIRCam imaging in GOODS-N and GOODS-S

TL;DR

This work delivers a large-scale morphological dataset by fitting single-component Sérsic profiles to over $10^5$ JWST/NIRCam sources in GOODS-N/S using Bayesian inference with the pysersic package, producing more than $3\times 10^6$ profile fits and full posterior distributions for structural parameters across eight wide-band filters. It leverages DR5 imaging and photometric catalogs to study rest-frame optical morphology and evolution, finding $r_{\mathrm{eff}} \propto (1+z)^{\beta_z}$ with $\beta_z = -0.635 \pm 0.013$ and a relatively constant $\Sigma_{\mathrm{1\,kpc}}$, while bulge-disk decomposition in HUDF shows slowly evolving bulges but rapidly growing disks with distinct scaling relations. The catalog provides a statistically robust framework for analyzing galaxy morphology across cosmic time, and forthcoming multi-component morphology catalogs will enable more detailed studies of bulge and disk growth, bars, and substructure in high-redshift galaxies. The dataset and methods established here will support cross-survey comparisons and cosmological inferences about galaxy assembly and structural evolution in the rest-frame optical regime.

Abstract

We present morphological parameters and their uncertainties for all sources detected in JWST/NIRCam imaging in GOODS-N and GOODS-S from the JWST Advanced Deep Extragalactic Survey (JADES) catalogs. We model the surface brightness profiles of these sources with single-component Sérsic profiles, performing Bayesian inference of galaxy structural parameters. We fit each of the $>10^5$ sources with every available JWST/NIRCam wide-band filter individually, amounting to over 3 million Sérsic profiles computed. We provide catalogs of this morphological information, building one of the largest extragalactic morphological datasets to date, which we share alongside imaging and photometry from the JADES Data Release 5. With this information, we analyze the rest-frame optical redshift evolution of the effective radius and the surface luminosity density within a radius of 1 kiloparsec, $Σ_{\text{1 kpc}}$, for 24,692 galaxies at $z>1$. We find $r_{\text{eff}} \propto (1+z)^{-0.635 \pm 0.013}$ kpc, while $Σ_{\text{1 kpc}}$ is relatively constant across time. Additionally, we explore bulge-disk decomposition on a subset of 8,390 galaxies in the JADES deep imaging covering the Hubble Ultra Deep Field, finding the effective radius of the bulge-components to increase marginally with time, whereas the disk-component sizes evolve as $r_{\text{eff,disk}} \propto (1+z)^{-1.091 \pm 0.043}$. Future work modeling multi-component surface brightness profiles will enable further analysis of the morphological evolution of galaxies across cosmic time.

Figures (13)

• Figure 1: Example of a successful single-component Sérsic fit to a spiral galaxy in GOODS-S. From left to right: (1, left) F444W/F200W/F090W false-color RGB image of JADES ID 172089, with $z_{\text{phot}}$ = 0.81 Robertson2026. (2) F444W image of this object. Scale bars along the bottom indicate the size in angular and physical units, in blue and red, respectively. The purple shaded region represents the segmentation associated with this object. (3) The best-fit Sérsic model for this object, as sampled by pysersic. The best-fit model is described the effective radius $r_{\text{eff}}$, Sérsic index $n$, and axis ratio $q$ listed in the image. (4, right) The residual of subtracting the Sérsic model from the F444W image. Underlying features, such as the spiral structure of the galaxy and a bright knot directly below the galactic nucleus, become clearly visible in the residual image.
• Figure 2: Injection-recovery simulation results from the (left) shallow, (middle) medium, and (right) deep imaging regions, respectively, in the JWST/NIRCam F444W imaging mosaic in GOODS-S. Each bin value, in text, indicates the median log(flux) in nJy at which the flux value input into the model is sufficiently recovered by pysersic, for that $n$ and $r_{\text{eff}}$ bin. The color bar indicates the SNR of that recovered model, calculated as the total flux within the segmentation map of the input model, before being added to the noisy sky region, divided by the total noise flux of the sky region within the segmentation map. Darker blue colors indicate that a higher SNR is required to recover the correct flux of the source.
• Figure 3: Example of an unsuccessful single-component Sérsic fit to a spiral galaxy in GOODS-S. From left to right: (1, left) F444W/F200W/F090W false-color RGB image of JADES ID 205449, with $z_{\text{phot}}$ = 1.1 Robertson2026. (2) F444W image of this object. Scale bars along the bottom indicate the size in angular and physical units, in blue and red, respectively. The purple shaded region represents the segmentation associated with this object. (3) The best-fit Sérsic model for this object, as sampled by pysersic. Though JADES ID 205449 appears visually as a spiral galaxy, which commonly have Sérsic indices $n\approx1$, the best-fit model finds $n=6.614$. (4, right) The residual of subtracting the Sérsic model from the F444W image. Here, we find a case where a single-component Sérsic model is clearly inadequate to describe this galaxy, given its bright nucleus alongside a flatter light distribution in the outer regions.
• Figure 4: Left: The rest-frame optical size evolution of the galaxies in our sample. We plot the data to highlight the density of galaxies, with darker blue colors representing a higher number of galaxies. The pink line shows the power-law parameterization fit to all data points, where $R=2.248 \pm 0.037$ kpc and $\beta_z= -0.635 \pm 0.013$. The turquoise points indicate the median $r_{\text{eff}}$ values in redshift bins of width $z=2$, with the error bars showing the 16th and 84th percentiles of the $r_{\text{eff}}$ distribution for that bin. Right: The pink line, showing the power-law fit to our data, and the turquoise points, showing our sample binned by redshift, are repeated from the left panel. We plot the power-law parameterizations found in other JWST- and HST-era analyses of the size evolution of galaxies vdw_2014shibuya_2015ormerod_2024ward_2024allen_2025yang_2025genin_2025; we note that the methodologies of each of these studies differ, which we discuss further in §\ref{['subsec:size_evo_discussion']}.
• Figure 5: The luminosity surface density within 1 kiloparsec, $\Sigma_{\text{1 kpc}}$, as a function of redshift. We plot the data to represent the density of galaxies, with darker blue representing a higher number of galaxies. The turquoise points indicate the median $\Sigma_{\text{1 kpc}}$ values in redshift bins of width $z=2$, with the error bars showing the 16th and 84th percentiles of the $\Sigma_{\text{1 kpc}}$ distribution for that bin.