A Self-Calibrating Halo-Based Group Finder: Application to SDSS

We apply a new galaxy group-finder to the Main Galaxy Sample of the SDSS. This algorithm introduces new freedom to assign halos to galaxies that is self-calibrated by comparing the catalog to complementary data. These include galaxy clustering data and measurements of the total satellite luminosity from deep-imaging data. We present constraints on the galaxy-halo connection for star-forming and quiescent populations. The results of the self-calibrated group catalog differ in several key ways from previous group catalogs and halo-occupation analyses. The transition halo mass scale, where half of the halos contain quiescent central galaxies, is at M h ∼ 1012.4 h −1 M ⊙, significantly higher than other constraints. Additionally, the width of the transition from predominantly star-forming halos to quiescent halos occurs over a narrower range in halo mass. Quiescent central galaxies in low-mass halos are significantly more massive than star-forming centrals at the same halo mass, but this difference reverses above the transition halo mass. We find that the scatter in log M * at fixed M h is ∼0.2 dex for massive halos, in agreement with previous estimates, but rises sharply at lower halo masses. The halo masses assigned by the group catalog are in good agreement with weak-lensing estimates for star-forming and quiescent central galaxies. We discuss possible improvements to the algorithm made clear by this first application to data. The group catalog is made publicly available.

Using a Neural Network Classifier to Select Galaxies with the Most Accurate Photometric Redshifts

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce several billion photometric redshifts (photo-z's), enabling cosmological analyses to select a subset of galaxies with the most accurate photo-z. We perform initial redshift fits on Subaru Strategic Program galaxies with deep grizy photometry using Trees for Photo-Z (TPZ) before applying a custom neural network classifier (NNC) tuned to select galaxies with (z phot − z spec)/(1 + z spec) < 0.10. We consider four cases of training and test sets ranging from an idealized case to using data augmentation to increase the representation of dim galaxies in the training set. Selections made using the NNC yield significant further improvements in outlier fraction and photo-z scatter (σ z ) over those made with typical photo-z uncertainties. As an example, when selecting the best third of the galaxy sample, the NNC achieves a 35% improvement in outlier rate and a 23% improvement in σ z compared to using uncertainties from TPZ. For cosmology and galaxy evolution studies, this method can be tuned to retain a particular sample size or to achieve a desired photo-z accuracy; our results show that it is possible to retain more than a third of an LSST-like galaxy sample while reducing σ z by a factor of 2 compared to the full sample, with one-fifth as many photo-z outliers. For surveys like LSST that are not limited by shot noise, this method enables a larger number of tomographic redshift bins and hence a significant increase in the total signal to noise of galaxy angular power spectra.

A MeerKAT 1.28 GHz Atlas of Southern Sources in the IRAS Revised Bright Galaxy Sample

The IRAS Revised Bright Galaxy Sample (RBGS) comprises galaxies and unresolved mergers stronger than S = 5.24 Jy at λ = 60 μm with Galactic latitudes ∣b∣ > 5°. Nearly all are dusty star-forming galaxies whose radio continuum and far-infrared luminosities are proportional to their current rates of star formation. We used the MeerKAT array of 64 dishes to make 5 × 3 minutes snapshot observations at ν = 1.28 GHz covering all 298 southern (J2000 δ < 0°) RBGS sources identified with external galaxies. The resulting images have θ ≈ 7.″5 FWHM resolution and rms fluctuations σ ≈ 20 μJy beam−1 ≈ 0.26 K low enough to reveal even faint disk emission. The rms position uncertainties are σ α ≈ σ δ ≈ 1″ relative to accurate near-infrared positions, and the image dynamic ranges are DR ≳ 104: 1. Cropped MeerKAT images of all 298 southern RBGS sources are available in FITS format from 10.48479/dnt7-6q05.

The mass and galaxy distribution around SZ-selected clusters

We present measurements of the radial profiles of the mass and galaxy number density around Sunyaev-Zel’dovich (SZ)-selected clusters using both weak lensing and galaxy counts. The clusters are selected from the Atacama Cosmology Telescope Data Release 5 and the galaxies from the Dark Energy Survey Year 3 dataset. With signal-to-noise of 62 (45) for galaxy (weak lensing) profiles over scales of about 0.2 − 20h−1 Mpc, these are the highest precision measurements for SZ-selected clusters to date. Because SZ selection closely approximates mass selection, these measurements enable several tests of theoretical models of the mass and light distribution around clusters. Our main findings are: 1. The splashback feature is detected at a consistent location in both the mass and galaxy profiles and its location is consistent with predictions of cold dark matter N-body simulations. 2. The full mass profile is also consistent with the simulations. 3. The shapes of the galaxy and lensing profiles are remarkably similar for our sample over the entire range of scales, from well inside the cluster halo to the quasilinear regime. We measure the dependence of the profile shapes on the galaxy sample, redshift and cluster mass. We extend the Diemer & Kravtsov model for the cluster profiles to the linear regime using perturbation theory and show that it provides a good match to the measured profiles. We also compare the measured profiles to predictions of the standard halo model and simulations that include hydrodynamics. Applications of these results to cluster mass estimation, cosmology and astrophysics are discussed.

Bright galaxy sample in the Kilo-Degree Survey Data Release 4

We present a bright galaxy sample with accurate and precise photometric redshifts (photo-zs), selected using ugriZYJHKs photometry from the Kilo-Degree Survey (KiDS) Data Release 4. The highly pure and complete dataset is flux-limited at r < 20 mag, covers ∼1000 deg2, and contains about 1 million galaxies after artifact masking. We exploit the overlap with Galaxy And Mass Assembly spectroscopy as calibration to determine photo-zs with the supervised machine learning neural network algorithm implemented in the ANNz2 software. The photo-zs have a mean error of |⟨δz⟩|∼5 × 10−4 and low scatter (scaled mean absolute deviation of ∼0.018(1 + z)); they are both practically independent of the r-band magnitude and photo-z at 0.05 < zphot < 0.5. Combined with the 9-band photometry, these allow us to estimate robust absolute magnitudes and stellar masses for the full sample. As a demonstration of the usefulness of these data, we split the dataset into red and blue galaxies, used them as lenses, and measured the weak gravitational lensing signal around them for five stellar mass bins. We fit a halo model to these high-precision measurements to constrain the stellar-mass–halo-mass relations for blue and red galaxies. We find that for high stellar mass (M⋆ > 5 × 1011 M⊙), the red galaxies occupy dark matter halos that are much more massive than those occupied by blue galaxies with the same stellar mass.

SEAGLE–II: Constraints on feedback models in galaxy formation from massive early-type strong lens galaxies

We use nine different galaxy formation scenarios in ten cosmological simulation boxes from the EAGLE suite of ΛCDM hydrodynamical simulations to assess the impact of feedback mechanisms in galaxy formation and compare these to observed strong gravitational lenses. To compare observations with simulations, we create strong lenses with M* &gt; 1011 M⊙ with the appropriate resolution and noise level, and model them with an elliptical power-law mass model to constrain their total mass density slope. We also obtain the mass-size relation of the simulated lens-galaxy sample. We find significant variation in the total mass density slope at the Einstein radius and in the projected stellar mass-size relation, mainly due to different implementations of stellar and AGN feedback. We find that for lens selected galaxies, models with either too weak or too strong stellar and/or AGN feedback fail to explain the distribution of observed mass-density slopes, with the counter-intuitive trend that increasing the feedback steepens the mass density slope around the Einstein radius (≈ 3-10 kpc). Models in which stellar feedback becomes inefficient at high gas densities, or weaker AGN feedback with a higher duty cycle, produce strong lenses with total mass density slopes close to isothermal (i.e. −dlog (ρ)/dlog (r) ≈ 2.0) and slope distributions statistically agreeing with observed strong lens galaxies in SLACS and BELLS. Agreement is only slightly worse with the more heterogeneous SL2S lens galaxy sample. Observations of strong-lens selected galaxies thus appear to favor models with relatively weak feedback in massive galaxies.

Testing Noncommutativity-Like Model as a Galactic Density Profile

Noncommutative-like model (NC-like) is an interesting alternative inspired by string theory to understand and describe the velocity rotation curves of galaxies without the inclusion of dark matter particles. In a natural way, a Gaussian density profile emerges and is characterized by a parameter θ, called the NC-like parameter. Hence we aim to confront the NC-like model with a galaxy sample of the Spitzer Photometry and Accurate Rotation Curves (SPARC) catalog to constrain the model parameters and compare statistically with the Einasto density profile using the Akaike and Bayesian information criteria. According to our results, some galaxies prefer the NC-like over the Einasto model while others do not support NC-like.