Galaxy formation and evolution

Abstract:

The physical processes that establish the morphological evolution and the structural diversity of galaxies are key unknowns in extragalactic astrophysics. Here we report the finding of the morphologically mature galaxy JADES-GS+53.18343−27.79097, which existed within the first 700 million years of the Universe’s history. This star-forming galaxy with a stellar mass of 400 million solar masses consists of three components: a highly compact core with a half-light radius of less than 100 pc, an actively star-forming disc with a radius of about 400 pc and a star-forming clump, all of which show distinctive star-formation histories. The central stellar mass density of this galaxy is within a factor of 2 of the most massive present-day ellipticals, while being globally 1,000 times less massive. The radial profile of the specific star-formation rate is rising towards the outskirts. This evidence suggests a detection of the inside-out growth of a galaxy as a proto-bulge and a star-forming disc in the epoch of reionization.

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.