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Velocity dispersion (σ) is a key driver for galaxy structure and evolution. We here present a comprehensive semi-empirical approach to compute σ via detailed Jeans modelling assuming both a constant and scale-dependent mass-to-light ratio M*/L. We compare with a large sample of local galaxies from MaNGA and find that both models can reproduce the Faber–Jackson (FJ) relation and the weak dependence of σ on bulge-to-total (B/T) ratio (for B/T ≳ 0.25). The dynamical-to-stellar mass ratio within R ≲ Re can be fully accounted for by a gradient in M*/L. We then build velocity dispersion evolutionary tracks σap[M*, z] (within an aperture) along the main progenitor dark matter haloes assigning stellar masses, effective radii, and Sérsic  indices via a variety of abundance matching and empirically motivated relations. We find: (1) clear evidence for downsizing in σap[M*, z] along the progenitor tracks; (2) at fixed stellar mass σ ∝ (1 + z)0.2−0.3 depending on the presence or not of a gradient in M*/L. We extract σap[M*, z] from the TNG50 hydrodynamic simulation and find very similar results to our models with constant M*/L. The increasing dark matter fraction within Re tends to flatten the σap[M*, z] along the progenitors at z ≳ 1 in constant M*/L models, while σap[M*, z] have a steeper evolution in the presence of a stellar gradient. We then show that a combination of mergers and gas accretion is likely responsible for the constant or increasing σap[M*, z] with time. Finally, our σap[M*, z] are consistent with a nearly constant and steep Mbh − σ relation at z ≲ 2, with black hole masses derived from the LX − M* relation.

 

ABSTRACT With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging data sets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect ${\sim}90{{\ \rm per\ cent}}$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models. The code used in this is available at https://github.com/carlamb/astronomical-outliers-WGAN. 

Abstract This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.