We investigate the (large-scale) bar fraction in a mass-complete sample of M > 10^10.5 Msun disk galaxies at 0.2 < z < 0.6 in the COSMOS field. The fraction of barred disks strongly depends on mass, disk morphology, and specific star formation rate (SSFR). At intermediate stellar mass (10^10.5 < M < 10^11 Msun) the bar fraction in early-type disks is much higher, at all redshifts, by a factor ~2, than that in late-type disks. This trend is reversed at higher stellar mass (M > 10^11 Msun), where the fraction of bars in early-type disks becomes significantly lower, at all redshifts, than that in late-type disks. The bar fractions for galaxies with low and high SSFRs closely follow those of the morphologically-selected early-type and late-type populations, respectively. This indicates a close correspondence between morphology and SSFR in disk galaxies at these earlier epochs. Interestingly, the total bar fraction in 10^10.5 < M < 10^11 Msun disks is built up by a factor of ~2 over the redshift interval explored, while for M > 10^11 Msun disks it remains roughly constant. This indicates that, already by z ~ 0.6, spectral and morphological transformations in the most massive disk galaxies have largely converged to the familiar Hubble sequence that we observe in the local Universe, while for intermediate mass disks this convergence is ongoing until at least z ~ 0.2. Moreover, these results highlight the importance of employing mass-limited samples for quantifying the evolution of barred galaxies. Finally, the evolution of the barred galaxy populations investigated does not depend on the large-scale environmental density (at least, on the scales which can be probed with the available photometric redshifts).
Understanding the relationship between galaxies hosting active galactic nuclei (AGN) and the dark matter halos in which they reside is key to constraining how black-hole fueling is triggered and regulated. Previous efforts have relied on simple halo mass estimates inferred from clustering, weak gravitational lensing, or halo occupation distribution modeling. In practice, these approaches remain uncertain because AGN, no matter how they are identified, potentially live a wide range of halo masses with an occupation function whose general shape and normalization are poorly known. In this work, we show that better constraints can be achieved through a rigorous comparison of the clustering, lensing, and cross-correlation signals of AGN hosts to a fiducial stellar-to-halo mass relation (SHMR) derived for all galaxies. Our technique exploits the fact that the global SHMR can be measured with much higher accuracy than any statistic derived from AGN samples alone. Using 382 moderate luminosity X-ray AGN at z<1 from the COSMOS field, we report the first measurements of weak gravitational lensing from an X-ray selected sample. Comparing this signal to predictions from the global SHMR, we find that, contrary to previous results, most X-ray AGN do not live in medium size groups ---nearly half reside in relatively low mass halos with Mh~10^12.5 Msun. The AGN occupation function is well described by the same form derived for all galaxies but with a lower normalization---the fraction of halos with AGN in our sample is a few percent. By highlighting the relatively "normal" way in which moderate luminosity X-ray AGN hosts occupy halos, our results suggest that the environmental signature of distinct fueling modes for luminous QSOs compared to moderate luminosity X-ray AGN is less obvious than previously claimed.
ABSTRACT Galaxy–galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) data set promise to yield unprecedented combined-probe tests of cosmology and the galaxy–halo connection. In such analyses, it is essential to identify and characterize all relevant statistical and systematic errors. We forecast the covariances of DESI Y1 GGL + clustering measurements and the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and GGL correlations, we compute a Gaussian analytical covariance, using a suite of N-body and lognormal simulations to characterize the effect of the survey footprint. Using the DESI one percent survey data, we measure the evolution of galaxy bias parameters for the DESI luminous red galaxy (LRG) and bright galaxy survey (BGS) samples. We find mild evolution in the LRGs in $0.4 < z < 0.8$, subdominant to the expected statistical errors. For BGS, we find less evolution for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that for a redshift bin width $\Delta z = 0.1$, evolution effects on DESI Y1 GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of $0.1h^{-1}$ Mpc. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample.
We conduct a comprehensive study of the effects of incorporating galaxy morphology information in photometric redshift estimation. Using machine learning methods, we assess the changes in the scatter and catastrophic outlier fraction of photometric redshifts when galaxy size, ellipticity, S\'{e}rsic index and surface brightness are included in training on galaxy samples from the SDSS and the CFHT Stripe-82 Survey (CS82). We show that by adding galaxy morphological parameters to full $ugriz$ photometry, only mild improvements are obtained, while the gains are substantial in cases where fewer passbands are available. For instance, the combination of $grz$ photometry and morphological parameters almost fully recovers the metrics of $5$-band photometric redshifts. We demonstrate that with morphology it is possible to determine useful redshift distribution $N(z)$ of galaxy samples without any colour information. We also find that the inclusion of quasar redshifts and associated object sizes in training improves the quality of photometric redshift catalogues, compensating for the lack of a good star-galaxy separator. We further show that morphological information can mitigate biases and scatter due to bad photometry. As an application, we derive both point estimates and posterior distributions of redshifts for the official CS82 catalogue, training on morphology and SDSS Stripe-82 $ugriz$ bands when available. Our redshifts yield a 68th percentile error of $0.058(1+z)$, and a catastrophic outlier fraction of $5.2$ per cent. We further include a deep extension trained on morphology and single $i$-band CS82 photometry.
We investigate how the shape of the galaxy two-point correlation function as measured in the zCOSMOS survey depends on local environment, quantified in terms of the density contrast on scales of 5 h−1 Mpc. We show that the flat shape previously observed at redshifts between z= 0.6 and 1 can be explained by this volume being simply 10 per cent overabundant in high-density environments, with respect to a universal density probability distribution function. When galaxies corresponding to the top 10 per cent tail of the distribution are excluded, the measured wp(rp) steepens and becomes indistinguishable from Lambda cold dark matter (ΛCDM) predictions on all scales. This is the same effect recognized by Abbas & Sheth in the Sloan Digital Sky Survey (SDSS) data at z≃ 0 and explained as a natural consequence of halo–environment correlations in a hierarchical scenario. Galaxies living in high-density regions trace dark matter haloes with typically higher masses, which are more correlated. If the density probability distribution function of the sample is particularly rich in high-density regions because of the variance introduced by its finite size, this produces a distorted two-point correlation function. We argue that this is the dominant effect responsible for the observed ‘peculiar’ clustering in the COSMOS field.
We derive the mass weighted total density slopes within the effective (half-light) radius, $\gamma'$, for more than 2000 nearby galaxies from the SDSS-IV MaNGA survey using Jeans-anisotropic-models applied to IFU observations. Our galaxies span a wide range of the stellar mass ($10^9$ $M_{\rm \odot}< M_* < 10^{12}$ M$_{\odot}$) and the velocity dispersion (30 km/s $< \sigma_v <$ 300 km/s). We find that for galaxies with velocity dispersion $\sigma_v>100$ km/s, the density slope has a mean value $\langle \gamma^{\prime} \rangle = 2.24$ and a dispersion $\sigma_{\gamma}=0.22$, almost independent of velocity dispersion. A clear turn over in the $\gamma'-\sigma_v$ relation is present at $\sigma\sim 100$ km/s, below which the density slope decreases rapidly with $\sigma_v$. Our analysis shows that a large fraction of dwarf galaxies (below $M_* = 10^{10}$ M$_{\odot}$) have total density slopes shallower than 1, which implies that they may reside in cold dark matter halos with shallow density slopes. We compare our results with that of galaxies in hydrodynamical simulations of EAGLE, Illustris and IllustrisTNG projects, and find all simulations predict shallower density slopes for massive galaxies with high $\sigma_v$. Finally, we explore the dependence of $\gamma'$ on the positions of galaxies in halos, namely centrals vs. satellites, and find that for the same velocity dispersion, the amplitude of $\gamma'$ is higher for satellite galaxies by about 0.1.
We use the UniverseMachine to analyze the source of scatter between the central galaxy mass, the total stellar mass in the halo, and the dark matter halo mass. We also propose a new halo mass estimator, the cen+N mass: the sum of the stellar mass of the central and the N most massive satellites. We show that, when real space positions are perfectly known, the cen+N mass has scatter competitive with that of richness-based estimators. However, in redshift space, the cen+N mass suffers less from projection effects in the UniverseMachine model. The cen+N mass is therefore a viable low scatter halo mass estimator, and should be considered an important tool to constrain cosmology with upcoming spectroscopic data from DESI. We analyze the scatter in stellar mass at fixed halo mass and show that the total stellar mass in a halo is uncorrelated with secondary halo properties, but that the central stellar mass is a function of both halo mass and halo age. This is because central galaxies in older halos have had more time to grow via accretion. If the UniverseMachine model is correct, accurate galaxy-halo modeling of mass selected samples therefore needs to consider halo age in addition to mass.
We present a novel simulation-based cosmological analysis of galaxy-galaxy lensing and galaxy redshift-space clustering. Compared to analysis methods based on perturbation theory, our simulation-based approach allows us to probe a much wider range of scales, $0.4 \, h^{-1} \, \mathrm{Mpc}$ to $63 \, h^{-1} \, \mathrm{Mpc}$, including highly non-linear scales, and marginalises over astrophysical effects such as assembly bias. We apply this framework to data from the Baryon Oscillation Spectroscopic Survey LOWZ sample cross-correlated with state-of-the-art gravitational lensing catalogues from the Kilo Degree Survey and the Dark Energy Survey. We show that gravitational lensing and redshift-space clustering when analysed over a large range of scales place tight constraints on the growth-of-structure parameter $S_8 = \sigma_8 \sqrt{\Omega_{\rm m} / 0.3}$. Overall, we infer $S_8 = 0.792 \pm 0.022$ when analysing the combination of galaxy-galaxy lensing and projected galaxy clustering and $S_8 = 0.771 \pm 0.027$ for galaxy redshift-space clustering. These findings highlight the potential constraining power of full-scale studies over studies analysing only large scales, and also showcase the benefits of analysing multiple large-scale structure surveys jointly. Our inferred values for $S_8$ fall below the value inferred from the CMB, $S_8 = 0.834 \pm 0.016$. While this difference is not statistically significant by itself, our results mirror other findings in the literature whereby low-redshift large scale structure probes infer lower values for $S_8$ than the CMB, the so-called $S_8$-tension.
We measure the spatial clustering of galaxies as a function of their morphological type at z≃ 0.8, for the first time in a deep redshift survey with full morphological information. This is obtained by combining high-resolution Hubble Space Telescope imaging and Very Large Telescope spectroscopy for about 8500 galaxies to with accurate spectroscopic redshifts from the zCOSMOS-Bright redshift survey. At this epoch, early-type galaxies already show a significantly stronger clustering than late-type galaxies on all probed scales. A comparison to the Sloan Digital Sky Survey Data at z≃ 0.1 shows that the relative clustering strength between early and late morphological classes tends to increase with cosmic time at small separations, while on large scales it shows no significant evolution since z≃ 0.8. This suggests that most early-type galaxies had already formed in intermediate and dense environments at this epoch. Our results are consistent with a picture in which the relative clustering of different morphological types between z≃ 1 and 0 reflects the evolving role of environment in the morphological transformation of galaxies, on top of a global evolution driven by mass.
The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of $\sim$13,000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity HII region. We have released a catalog with the WGAN anomaly scores; the code and catalog are available at https://github.com/kstoreyf/anomalies-GAN-HSC, and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.
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