We present catalogues of stellar masses, star formation rates, and ancillary stellar population parameters for galaxies spanning 0 < z < 9 from the Deep Extragalactic VIsible Legacy Survey (DEVILS). DEVILS is a deep spectroscopic redshift survey with very high completeness, covering several premier deep fields including COSMOS (D10). Our stellar mass and star formation rate estimates are self-consistently derived using the spectral energy distribution (SED) modelling code ProSpect, using well-motivated parameterisations for dust attenuation, star formation histories, and metallicity evolution. We show how these improvements, and especially our physically motivated assumptions about metallicity evolution, have an appreciable systematic effect on the inferred stellar masses, at the level of ∼ 0.2 dex. To illustrate the scientific value of these data, we map the evolving galaxy stellar mass function (SMF) and the SFR-M⋆ relation for 0 < z < 4.25. In agreement with past studies, we find that most of the evolution in the SMF is driven by the characteristic density parameter, with little evolution in the characteristic mass and low-mass slopes. Where the SFR-M⋆ relation is indistinguishable from a power-law at z > 2.6, we see evidence of a bend in the relation at low redshifts (z < 0.45). This suggests evolution in both the normalisation and shape of the SFR-M⋆ relation since cosmic noon. It is significant that we only clearly see this bend when combining our new DEVILS measurements with consistently derived values for lower redshift galaxies from the Galaxy And Mass Assembly (GAMA) survey: this shows the power of having consistent treatment for galaxies at all redshifts.

We use the full VIPERS redshift survey in combination with SDSS-DR7 to explore the relationships between star-formation history (using d4000), stellar mass and galaxy structure, and how these relationships have evolved since z ∼ 1. We trace the extents and evolutions of both the blue cloud and red sequence by fitting double Gaussians to the d4000 distribution of galaxies in narrow stellar mass bins, for four redshift intervals over 0 < z < 1. This reveals downsizing in star formation, since the high-mass limit of the blue cloud has retreated steadily with time from M ∼ 10 11.2 M at z ∼ 0.9 to M ∼ 10 10.7 M by the present day. The number density of massive blue-cloud galaxies (M > 10 11 M , d4000 < 1.55) drops sharply by a factor five between z ∼ 0.8 and z ∼ 0.5. These galaxies are becoming quiescent at a rate that largely matches the increase in the numbers of massive passive galaxies seen over this period. We examine the size-mass relation of blue-cloud galaxies, finding that its high-mass boundary runs along lines of constant M/r e or equivalently inferred velocity dispersion. Larger galaxies can continue to form stars to higher stellar masses than smaller galaxies. As blue-cloud galaxies approach this high-mass limit, entering a narrow diagonal region within the size-mass plane termed the "quenching zone", they start to be quenched, their d4000 values increasing to push them towards the green valley. In parallel, their structures change, showing higher Sérsic indices and central stellar mass densities. For these galaxies, bulge growth is required for them to reach the high-mass limit of the blue cloud and be quenched by internal mechanisms. The blue-cloud galaxies that are being quenched at z ∼ 0.8 lie along the same size-mass relation as present day quiescent galaxies and seem the likely progenitors of today's S0s.

Aims. Various galaxy classification schemes have been developed so far to constrain the main physical processes regulating evolution of different galaxy types. In the era of a deluge of astrophysical information and recent progress in machine learning, a new approach to galaxy classification has become imperative. Methods. In this paper, we employ a Fisher Expectation-Maximization (FEM) unsupervised algorithm working in a parameter space of 12 restframe magnitudes and spectroscopic redshift. The model (DBk) and the number of classes (12) were established based on the joint analysis of standard statistical criteria and confirmed by the analysis of the galaxy distribution with respect to a number of classes and their properties. This new approach allows us to classify galaxies based on only their redshifts and ultraviolet to near-infrared (UV-NIR) spectral energy distributions. Results. The FEM unsupervised algorithm has automatically distinguished 12 classes: 11 classes of VIPERS galaxies and an additional class of broad-line active galactic nuclei (AGNs). After a first broad division into blue, green, and red categories, we obtained a further sub-division into: three red, three green, and five blue galaxy classes. The FEM classes follow the galaxy sequence from the earliest to the latest types, which is reflected in their colours (which are constructed from rest-frame magnitudes used in the classification procedure) but also their morphological, physical, and spectroscopic properties (not included in the classification scheme). We demonstrate that the members of each class share similar physical and spectral properties. In particular, we are able to find three different classes of red passive galaxy populations. Thus, we demonstrate the potential of an unsupervised approach to galaxy classification and we retrieve the complexity of galaxy populations at z ∼ 0.7, a task that usual, simpler, colour-based approaches cannot fulfil.