The fourth generation of the Sloan

No abstract

We study radial profiles in Hα equivalent width and specific star formation rate (sSFR) derived from spatially-resolved SDSS-IV MaNGA spectroscopy to gain insight on the physical mechanisms that suppress star formation and determine a galaxy's location in the SFR-M diagram. Even within the star-forming 'main sequence', the measured sSFR decreases with stellar mass, both in an integrated and spatially-resolved sense. Flat sSFR radial profiles are observed for log(M /M ) < 10.5, while star-forming galaxies of higher mass show a significant decrease in sSFR in the central regions, a likely consequence of both larger bulges and an inside-out growth history. Our primary focus is the green valley, constituted by galaxies lying below the star formation main sequence, but not fully passive. In the green valley we find sSFR profiles that are suppressed with respect to star-forming galaxies of the same mass at all galactocentric distances out to 2 effective radii. The responsible quenching mechanism therefore appears to affect the entire galaxy, not simply an expanding central region. The majority of green valley galaxies of log(M /M ) > 10.0 are classified spectroscopically as central low-ionisation emission-line regions (cLIERs). Despite displaying a higher central stellar mass concentration, the sSFR suppression observed in cLIER galaxies is not simply due to the larger mass of the bulge. Drawing a comparison sample of star forming galaxies with the same M and Σ 1 kpc (the mass surface density within 1 kpc), we show that a high Σ 1 kpc is not a sufficient condition for determining central quiescence.

We study the spatially resolved star formation of 1494 galaxies in the SDSSIV-MaNGA Survey. SFRs are calculated using a two-step process, using H α in star forming regions and D n 4000 in regions identified as AGN/LI(N)ER or lineless. The roles of secular and environmental quenching processes are investigated by studying the dependence of the radial profiles of specific star formation rate on stellar mass, galaxy structure and environment. We report on the existence of 'Centrally Suppressed' galaxies, which have suppressed SSFR in their cores compared to their disks. The profiles of centrally suppressed and unsuppressed galaxies are distibuted in a bimodal way. Galaxies with high stellar mass and core velocity dispersion are found to be much more likely to be centrally suppressed than low mass galaxies, and we show that this is related to morphology and the presence of AGN/LI(N)ER like emission. Centrally suppressed galaxies also display lower star formation at all radii compared to unsuppressed galaxies. The profiles of central and satellite galaxies are also compared, and we find that satellite galaxies experience lower specific star formation rates at all radii than central galaxies. This uniform suppression could be a signal of the stripping of hot halo gas in the process known as strangulation. We find that satellites are not more likely to be suppressed in their cores than centrals, indicating that the core suppression is an entirely internal process. We find no correlation between the local environment density and the profiles of star formation rate surface density.

We present AstroVaDEr, a variational autoencoder designed to perform unsupervised clustering and synthetic image generation using astronomical imaging catalogues. The model is a convolutional neural network that learns to embed images into a low dimensional latent space, and simultaneously optimises a Gaussian Mixture Model (GMM) on the embedded vectors to cluster the training data. By utilising variational inference, we are able to use the learned GMM as a statistical prior on the latent space to facilitate random sampling and generation of synthetic images. We demonstrate AstroVaDEr’s capabilities by training it on gray-scaled gri images from the Sloan Digital Sky Survey, using a sample of galaxies that are classified by Galaxy Zoo 2. An unsupervised clustering model is found which separates galaxies based on learned morphological features such as axis ratio, surface brightness profile, orientation and the presence of companions. We use the learned mixture model to generate synthetic images of galaxies based on the morphological profiles of the Gaussian components. AstroVaDEr succeeds in producing a morphological classification scheme from unlabelled data, but unexpectedly places high importance on the presence of companion objects—demonstrating the importance of human interpretation. The network is scalable and flexible, allowing for larger datasets to be classified, or different kinds of imaging data. We also demonstrate the generative properties of the model, which allow for realistic synthetic images of galaxies to be sampled from the learned classification scheme. These can be used to create synthetic image catalogs or to perform image processing tasks such as deblending.