JADES: Low Surface Brightness Galaxies at 0.4 < z < 0.8 in GOODS-S

TL;DR

This study leverages JWST/NIRCam imaging from JADES in GOODS-S to identify a population of low surface brightness galaxies (LSBs) at $0.4 < z_{ m phot} < 0.8$ by selecting objects with $\bar{\mu}_{\rm eff}({\rm F200W}) > 24$ mag arcsec$^{-2}$ and fitting their 2D light profiles with Sérsic models. Using photometric redshifts from eazy-py and surface-brightness fits from pyimfit and pysersic, the authors assemble a sample of 57 LSBs, derive multi-band structural parameters, colours, and rest-frame properties, and compare them to higher-surface-brightness (HSB) galaxies at similar mass and to lower-redshift LSBs with BAGPIPES SED fitting. The analysis shows LSBs in this redshift range are dwarf, star formation–quenched systems with typically low dust attenuation ($A_V < 1$ mag) and low SFR over the last 100 Myr ($\rm SFR_{100} \lesssim 0.01\,M_\odot\,{ m yr}^{-1}$), consistent with a scenario where LSBs and HSBs share progenitors at $z \gtrsim 2$ but diverge due to feedback, tidal interactions, and environmental processes. JWST enables probing LSBs well beyond the local Universe, highlighting the importance of deep, high-resolution, multi-band data for understanding dwarf galaxy evolution and the role of surface brightness in tracing galaxy growth.

Abstract

Low surface brightness galaxies (LSBs) are an important class of galaxies that allow us to broaden our understanding of galaxy formation and test various cosmological models. We present a survey of low surface brightness galaxies at $0.4 < z_{\rm phot} < 0.8$ in the GOODS-S field using JADES data. We model LSB surface brightness profiles, identifying those with $\barμ_{\rm eff} > 24$ mag arcsec$^{-2}$ in the F200W JWST/NIRCam filter. We study the spatial distribution, number density, Sérsic profile parameters, and rest-frame colours of these LSBs. We compare the photometrically-derived star formation histories, mass-weighted ages, and dust attenuations of these galaxies with a high surface brightness (HSB) sample at similar redshift and a lower redshift ($z_{\rm phot} < 0.4$) LSB sample, all of which have stellar masses $\lesssim 10^8 M_{\odot}$. We find that both the high and the low redshift LSB samples have low star formation (SFR$_{100} \lesssim 0.01$ $M_{\odot}$ yr$^{-1}$) compared with the HSB sample (SFR$_{100} \gtrsim 0.01$ $M_{\odot}$ yr$^{-1}$). The star formation histories show that the LSBs and HSBs possibly come from the same progenitors at $z \gtrsim 2$, though the histories are not well constrained for the LSB samples. The LSBs appear to have minimal dust, with most of our LSB samples showing $A_V < 1$ mag. JWST has pushed our understanding of LSBs beyond the local Universe.

Figures (14)

• Figure 1: Example photometric redshift SED fits from eazy-py shown for four LSB candidates. Small Kron photometry from NIRCam filters from the catalogue described in Section \ref{['sec:data']} is shown in red with measured errors, while those from HST filters are shown in pink, both in nJy at the pivot wavelength (in microns) of the corresponding filter. The best-fit SED template from eazy-py is shown as a blue line. Each SED has an inset plot showing the probability $p(z)$ surface for the fit, where our adopted redshift $z_a$ (corresponding to the maximum $p(z)$), is shown as a red vertical line and the area between the 1$\sigma$ uncertainties of the distribution are shaded in gray. The redshift values corresponding to $z_a$ and the 1$\sigma$ spread are printed at the top of each inset plot. A mosaic cutout for each object is shown in six wide NIRCam filters below each SED.
• Figure 2: pyimfit 2D surface brightness profiles for four LSB candidates chosen randomly from bins of the Sérsic index $n$. From top to bottom, the rows show the original cutout from the F200W mosaic, the best-fit Sérsic model, the residual (data minus model), and the profile plotted against the data, by drawing many ellipses at different radii, to the object's fitted $R_{\rm eff}$. The white pixels were determined to belong to another object within the cutout, and were masked from the fits. Each object's JADES ID, $n$, and $R_{\rm eff}$ are printed in each model panel, as well as an angular scale corresponding to $0.3^{\prime\prime}$ for each object. Cutouts are 12 times the object's catalogue semi-major axis.
• Figure 3: The mean effective surface brightness given by Eqn \ref{['eq:mean_eff_sb']} from pyimfit in the F200W filter (corrected for cosmological dimming with photometric redshifts from eazy-py) against the photometric redshift from eazy-py for each object with SNR > 10 in F200W, fitted effective radius less than 1.5 times their semi-major axis, and fitted effective radius greater than 0.18 arcsec. A vertical dashed line is drawn at redshift $z_{\rm phot}=0.4$ and a horizontal dashed line is drawn at $\bar{\mu}_{\rm eff} = 24$ mag arcsec$^{-2}$, both in red. The LSB sample, objects $z_{\rm phot} > 0.4$ and $\bar{\mu}_{\rm eff} > 24$ mag arcsec$^{-2}$, is highlighted in red.
• Figure 4: False-colour RGB (F277W/F200W/F115W) images for the 20 largest LSBs in the sample, by effective radii. Each cutout is 50 x 50 pixels (1.5 x 1.5 arcsec), and each object is normalised by its individual cutout's brightest pixel. Each object's JADES catalogue ID and photometric redshift are shown in the lower left corners, while the physical scale corresponding to 0.3 arcsec is provided in the top right corners.
• Figure 5: Distribution of photometric redshifts obtained through eazy-py (as described in Section \ref{['sec:selection']}) for our final sample of LSBs.