Context. We present the full data set of the VIsible Multi-Object Spectrograph (VIMOS) spectroscopic campaign of the ESO/GOODS program in the Chandra Deep Field South (CDFS), which complements the FORS2 ESO/GOODS spectroscopic campaign. Aims. The ESO/GOODS spectroscopic programs are aimed at reaching signal-to-noise ratios adequate to measure redshifts for galaxies with AB magnitudes in the range ∼24−25 in the B and R band using VIMOS, and in the z band using FORS2. Methods. The GOODS/VIMOS spectroscopic campaign is structured in two separate surveys using two different VIMOS grisms. The VIMOS Low Resolution Blue (LR-Blue) and Medium Resolution (MR) orange grisms have been used to cover different redshift ranges. The LR-Blue campaign is aimed at observing galaxies mainly at 1.8 < z < 3.5, while the MR campaign mainly aims at galaxies at z < 1 and Lyman Break Galaxies (LBGs) at z > 3.5. Results. The full GOODS/VIMOS spectroscopic campaign consists of 20 VIMOS masks. This release adds 8 new masks to the previous release (12 masks, Popesso et al. 2009, A&A, 494, 443). In total we obtained 5052 spectra, 3634 from the 10 LR-Blue masks and 1418 from the 10 MR masks. A significant fraction of the extracted spectra comes from serendipitously observed sources: ∼21% in the LR-Blue and ∼16% in the MR masks. We obtained 2242 redshifts in the LR-Blue campaign and 976 in the MR campaign for a total success rate of 62% and 69% respectively, which increases to 66% and 73% if only primary targets are considered. The typical redshift uncertainty is estimated to be σ z 0.00084 (∼255 km s −1 ) for the LR-Blue grism and σ z 0.00040 (∼120 km s −1 ) for the MR grism. By complementing our VIMOS spectroscopic catalog with all existing spectroscopic redshifts publicly available in the CDFS, we compiled a redshift master catalog with 7332 entries, which we used to investigate large scale structures out to z 3.7. We produced stacked spectra of LBGs in a few bins of equivalent width (EW) of the Ly-α and found evidence for a lack of bright LBGs with high EW of the Ly-α. Finally, we obtained new redshifts for 12 X-ray sources of the CDFS and extended-CDFS. Conclusions. After the completion of the two complementary ESO/GOODS spectroscopic campaigns with VIMOS and FORS2 at VLT, the number of spectroscopic redshifts in CDFS/GOODS field increased dramatically, in particular at z > ∼ 2. These data provide the redshift information indispensable to achieve the scientific goals of GOODS, such as tracing the evolution of galaxy masses, morphologies, clustering, and star formation.

We present an analysis of star formation and quenching in the SDSS-IV MaNGA-DR15, utilising over 5 million spaxels from ∼3500 local galaxies. We estimate star formation rate surface densities (Σ SFR ) via dust corrected Hα flux where possible, and via an empirical relationship between specific star formation rate (sSFR) and the strength of the 4000Å break (D4000) in all other cases. We train a multi-layered artificial neural network (ANN) and a random forest (RF) to classify spaxels into 'star forming' and 'quenched' categories given various individual (and groups of) parameters. We find that global parameters (pertaining to the galaxy as a whole) perform collectively the best at predicting when spaxels will be quenched, and are substantially superior to local/ spatially resolved and environmental parameters. Central velocity dispersion is the best single parameter for predicting quenching in central galaxies. We interpret this observational fact as a probable consequence of the total integrated energy from AGN feedback being traced by the mass of the black hole, which is well known to correlate strongly with central velocity dispersion. Additionally, we train both an ANN and RF to estimate Σ SFR values directly via regression in star forming regions. Local/ spatially resolved parameters are collectively the most predictive at estimating Σ SFR in these analyses, with stellar mass surface density at the spaxel location (Σ * ) being by far the best single parameter. Thus, quenching is fundamentally a global process but star formation is governed locally by processes within each spaxel.

We investigate how star formation quenching proceeds within central and satellite galaxies using spatially resolved spectroscopy from the SDSS-IV MaNGA DR15. We adopt a complete sample of star formation rate surface densities (ΣSFR), derived in Bluck et al. (2020), to compute the distance at which each spaxel resides from the resolved star forming main sequence (ΣSFR − Σ* relation): ΔΣSFR. We study galaxy radial profiles in ΔΣSFR, and luminosity weighted stellar age (AgeL), split by a variety of intrinsic and environmental parameters. Via several statistical analyses, we establish that the quenching of central galaxies is governed by intrinsic parameters, with central velocity dispersion (σc) being the most important single parameter. High mass satellites quench in a very similar manner to centrals. Conversely, low mass satellite quenching is governed primarily by environmental parameters, with local galaxy over-density (δ5) being the most important single parameter. Utilising the empirical MBH - σc relation, we estimate that quenching via AGN feedback must occur at MBH ≥ 106.5 − 7.5M⊙, and is marked by steeply rising ΔΣSFR radial profiles in the green valley, indicating ‘inside-out’ quenching. On the other hand, environmental quenching occurs at over-densities of 10 - 30 times the average galaxy density at z∼0.1, and is marked by steeply declining ΔΣSFR profiles, indicating ‘outside-in’ quenching. Finally, through an analysis of stellar metallicities, we conclude that both intrinsic and environmental quenching must incorporate significant starvation of gas supply.

We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a population of over 400,000 central galaxies taken from the Sloan Digital Sky Survey Data Release 7. We utilise a variety of physical galaxy properties for training the pattern recognition algorithm to recognise star forming and passive systems, for a 'training set' of ∼100,000 galaxies. We then apply the ANN model to a 'verification set' of ∼100,000 different galaxies, randomly chosen from the remaining sample. The success rate of each parameter singly, and in conjunction with other parameters, is taken as an indication of how important the parameters are to the process(es) of central galaxy quenching. We find that central velocity dispersion, bulge mass and B/T are excellent predictors of the passive state of the system, indicating that properties related to the central mass of the galaxy are most closely linked to the cessation of star formation. Larger scale galaxy properties (total or disk stellar masses), or those linked to environment (halo masses or δ 5 ) perform significantly less well. Our results are plausibly explained by AGN feedback driving the quenching of central galaxies, although we discuss other possibilities as well.

The Canada-France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ~5000 square degrees of the sky, representing a first-rate opportunity to identify recently-merged galaxies. Due to the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training samples of post-merger and isolated galaxy images are generated from the IllustrisTNG simulation processed with the observational realism code RealSim. The CNN’s overall classification accuracy is 88 percent, remaining stable over a wide range of intrinsic and environmental parameters. We generate a mock galaxy survey from IllustrisTNG in order to explore the expected purity of post-merger samples identified by the CNN. Despite the CNN’s good performance in training, the intrinsic rarity of post-mergers leads to a sample that is only ~6 percent pure when the default decision threshold is used. We investigate trade-offs in purity and completeness with a variable decision threshold and find that we recover the statistical distribution of merger-induced star formation rate enhancements. Finally, the performance of the CNN is compared with both traditional automated methods and human classifiers. The CNN is shown to outperform Gini-M20 and asymmetry methods by an order of magnitude in post-merger sample purity on the mock survey data. Although the CNN outperforms the human classifiers on sample completeness, the purity of the post-merger sample identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.

Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observations and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semi-realistic images), (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semi-real images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1% classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps (87.1% compared to 79.6% accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data (86.0% with ronly compared to 87.1% with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, RealSim, as a companion to this paper.

Using artificial neural network (ANN) predictions of total infra-red luminosities (L IR ), we compare the host galaxy star formation rates (SFRs) of ∼ 21,000 optically selected active galactic nuclei (AGN), 466 low excitation radio galaxies (LERGs) and 721 mid-IR selected AGN. SFR offsets (∆ SFR) relative to a sample of star-forming 'main sequence' galaxies (matched in M , z and local environment) are computed for the AGN hosts. Optically selected AGN exhibit a wide range of ∆SFR, with a distribution skewed to low SFRs and a median ∆ SFR = −0.06 dex. The LERGs have SFRs that are shifted to even lower values with a median ∆ SFR = −0.5 dex. In contrast, mid-IR selected AGN have, on average, SFRs enhanced by a factor ∼ 1.5. We interpret the different distributions of ∆ SFR amongst the different AGN classes in the context of the relative contribution of triggering by galaxy mergers. Whereas the LERGs are predominantly fuelled through low accretion rate secular processes which are not accompanied by enhancements in SFR, mergers, which can simultaneously boost SFRs, most frequently lead to powerful, obscured AGN.

We investigate the dependence of galaxy structure on a variety of galactic and environmental parameters for ∼500,000 galaxies at z<0.2, taken from the Sloan Digital Sky Survey data release 7 (SDSS-DR7). We utilise bulge-to-total stellar mass ratio, (B/T) * , as the primary indicator of galactic structure, which circumvents issues of morphological dependence on waveband. We rank galaxy and environmental parameters in terms of how predictive they are of galaxy structure, using an artificial neural network approach. We find that distance from the star forming main sequence (∆SFR), followed by stellar mass (M * ), are the most closely connected parameters to (B/T) * , and are significantly more predictive of galaxy structure than global star formation rate (SFR), or any environmental metric considered (for both central and satellite galaxies). Additionally, we make a detailed comparison to the Illustris hydrodynamical simulation and the LGalaxies semi-analytic model. In both simulations, we find a significant lack of bulge-dominated galaxies at a fixed stellar mass, compared to the SDSS. This result highlights a potentially serious problem in contemporary models of galaxy evolution.