The spectroscopy and H-band imaging of Virgo cluster galaxies (SHIVir) survey: data catalogue and kinematic profiles
The ‘Spectroscopy and H-band Imaging of Virgo cluster galaxies’ (SHIVir) survey is an optical and near-infrared survey which combines SDSS photometry, deep H-band photometry, and long-slit optical spectroscopy for 190 Virgo cluster galaxies covering all morphological types over the stellar mass range log (M*/M⊙) = 7.8–11.5. We present the spectroscopic sample selection, data reduction, and analysis for this SHIVir sample. We have used and optimized the pPXF routine to extract stellar kinematics from our data. Ultimately, resolved kinematic profiles (rotation curves and velocity dispersion profiles) are available for 133 SHIVir galaxies. A comprehensive data base of photometric and kinematic parameters for the SHIVir sample is presented with grizH magnitudes, effective surface brightnesses, effective and isophotal radii, rotational velocities, velocity dispersions, and stellar and dynamical masses. Parameter distributions highlight some bimodal distributions and possible sample biases. A qualitative study of resolved extended velocity dispersion profiles suggests a link between the so-called ‘sigma-drop’ kinematic profile and the presence of rings in lenticular S0 galaxies. Rising dispersion profiles are linked to early-type spirals or dwarf ellipticals for which a rotational component is significant, whereas peaked profiles are tied to featureless giant ellipticals.
The scatter in the galaxy–halo connection: a machine learning analysis
We apply machine learning, a powerful method for uncovering complex correlations in high-dimensional data, to the galaxy–halo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic in the absence of perfect information, but conventional machine learning models are deterministic and hence cannot capture its intrinsic scatter. To overcome this limitation, we design an ensemble of neural networks with a Gaussian loss function that predict probability distributions, allowing us to model statistical uncertainties in the galaxy–halo connection as well as its best-fit trends. We extract a number of galaxy and halo variables from the Horizon-AGN and IllustrisTNG100-1 simulations and quantify the extent to which knowledge of some subset of one enables prediction of the other. This allows us to identify the key features of the galaxy–halo connection and investigate the origin of its scatter in various projections. We find that while halo properties beyond mass account for up to 50 per cent of the scatter in the halo-to-stellar mass relation, the prediction of stellar half-mass radius or total gas mass is not substantially improved by adding further halo properties. We also use these results to investigate semi-analytic models for galaxy size in the two simulations, finding that assumptions relating galaxy size to halo size or spin are not successful.