Galaxy formation and evolution

Abstract:

Context

The William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph that allows us to collect about one thousand spectra over a 3 square degree field in one observation. The WEAVE Stellar Population Survey (WEAVE-StePS) in the next 5 years will exploit this new instrument to obtain high-S/N spectra for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies at moderate redshifts (z ≥ 0.3), providing insights into galaxy evolution in this as yet unexplored redshift range.

Aims

We aim to test novel techniques for retrieving the key physical parameters of galaxies from WEAVE-StePS spectra using both photometric and spectroscopic (spectral indices) information for a range of noise levels and redshift values.

Methods

We simulated ∼105 000 galaxy spectra assuming star formation histories with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, specific star formation rates (sSFRs), and dust extinction values. We considered three redshifts (i.e. z = 0.3, 0.55, and 0.7), covering the redshift range that WEAVE-StePS will observe. We then evaluated the ability of the random forest and K-nearest neighbour algorithms to correctly predict the average age, metallicity, sSFR, dust attenuation, and time since the bulk of formation, assuming no measurement errors. We also checked how much the predictive ability deteriorates for different noise levels, with S/NI,obs = 10, 20, and 30, and at different redshifts. Finally, the retrieved sSFR was used to classify galaxies as part of the blue cloud, green valley, or red sequence.

Results

We find that both the random forest and K-nearest neighbour algorithms accurately estimate the mass-weighted ages, u-band-weighted ages, and metallicities with low bias. The dispersion varies from 0.08–0.16 dex for age and 0.11–0.25 dex for metallicity, depending on the redshift and noise level. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR ≳ −11, where the bias is ∼0.01 dex and the dispersion is ∼0.10 dex. However, for more quiescent galaxies, with log sSFR ≲ −11, we find a higher bias, ranging from 0.61 to 0.86 dex, and a higher dispersion, ∼0.4 dex, depending on the noise level and redshift. In general, we find that the random forest algorithm outperforms the K-nearest neighbours. Finally, we find that the classification of galaxies as members of the green valley is successful across the different redshifts and S/Ns.

Conclusions

We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies for a WEAVE-StePS-like dataset, even at relatively low S/NI, obs = 10 per Å spectra with available ancillary photometric information. A more traditional approach, Bayesian inference, yields comparable results. The main advantage of using a machine learning algorithm is that, once trained, it requires considerably less time than other methods.

Abstract:

We present a spatially resolved study of stellar populations in six galaxies with stellar masses M * ∼ 1010 M ☉ at z ∼ 3.7 using 14-filter James Webb Space Telescope (JWST)/NIRCam imaging from the JADES and JEMS surveys. The six galaxies are visually selected to have clumpy substructures with distinct colors over rest frame 3600−4100 Å, including a red, dominant stellar core that is close to their stellar-light centroids. With 23-filter photometry from the Hubble Space Telescope to JWST, we measure the stellar-population properties of individual structural components via spectral energy distribution fitting using Prospector. We find that the central stellar cores are ≳2 times more massive than the Toomre mass, indicating they may not form via single in situ fragmentation. The stellar cores have stellar ages of 0.4−0.7 Gyr that are similar to the timescale of clump inward migration due to dynamical friction, suggesting that they likely instead formed through the coalescence of giant stellar clumps. While they have not yet quenched, the six galaxies are below the star-forming main sequence by 0.2−0.7 dex. Within each galaxy, we find that the specific star formation rate is lower in the central stellar core, and the stellar-mass surface density of the core is already similar to quenched galaxies of the same masses and redshifts. Meanwhile, the stellar ages of the cores are either comparable to or younger than the extended, smooth parts of the galaxies. Our findings are consistent with model predictions of the gas-rich compaction scenario for the buildup of galaxies’ central regions at high redshifts. We are likely witnessing the coeval formation of dense central cores, along with the onset of galaxy-wide quenching at z > 3.

Abstract:

We conduct a systematic search for high-redshift galaxy overdensities at 4.9 < z spec < 8.9 in both the Great Observatories Origins Deep Survey (GOODS)-N and GOODS-S fields using James Webb Space Telescope/Near-Infrared Camera (JWST/NIRCam) imaging from the JWST Advanced Deep Extragalactic Survey and JWST Extragalactic Medium-band Survey in addition to JWST/NIRCam wide field slitless spectroscopy from the First Reionization Epoch Spectroscopic Complete Survey. High-redshift galaxy candidates are identified using Hubble Space Telescope + JWST photometry spanning λ = 0.4–5.0 μm. We confirmed the redshifts for roughly a third of these galaxies using JWST spectroscopy over λ = 3.9–5.0 μm through identification of either Hα or OIIIλ5008 around the best-fit photometric redshift. The rest-ultraviolet magnitudes and continuum slopes of these galaxies were inferred from the photometry: the brightest and reddest objects appear in more dense environments and thus are surrounded by more galaxy neighbors than their fainter and bluer counterparts, suggesting accelerated galaxy evolution within overdense environments. We find 17 significant (δ gal ≥ 3.04, N gal ≥ 4) galaxy overdensities across both fields (seven in GOODS-N and 10 in GOODS-S), including the two highest redshift spectroscopically confirmed galaxy overdensities to date at zspec=7.954 and zspec=8.222 (representing densities around ∼6 and ∼12 times that of a random volume). We estimate the total halo mass of these large-scale structures to be 11.5≤log10Mhalo/M⊙≤13.4 using an empirical stellar mass-to-halo mass relation, which are likely underestimates as a result of incompleteness. These protocluster candidates are expected to evolve into massive galaxy clusters with log10Mhalo/M⊙≳14 by z = 0.

Abstract:

One of the more surprising findings after the first year of JWST observations is the large number of spatially extended galaxies (ultrared flattened objects, or UFOs) among the optically faint galaxy (OFG) population otherwise thought to be compact. Leveraging the depth and survey area of the JWST Advanced Deep Extragalactic Survey, we extend observations of the OFG population to an additional 112 objects, 56 of which are well-resolved in F444W with effective sizes, R e > 0.″25, more than tripling previous UFO counts. These galaxies have redshifts around 2 < z < 4, high stellar masses ( log(M*/M⊙)∼10–11 ), and star formation rates around ∼100–1000 M ⊙ yr−1. Surprisingly, UFOs are red across their entire extents, which spatially resolved analysis of their stellar populations shows is due to large values of dust attenuation (typically A V > 2 mag even at large radii). Morphologically, the majority of our UFO sample tends to have low Sérsic indices (n ∼ 1) suggesting that these large, massive, OFGs have little contribution from a bulge in F444W. Further, a majority have axis ratios between 0.2 < q < 0.4, which Bayesian modeling suggests that their intrinsic shapes are consistent with being a mixture of inclined disks and prolate objects with little to no contribution from spheroids. While kinematic constraints will be needed to determine the true intrinsic shapes of UFOs, it is clear that an unexpected population of large, disky or prolate objects contributes significantly to the population of OFGs.