Constraints on Field Galaxy Halos from Weak Lensing and Satellite Dynamics

Here I summarize constraints on the nature of the dark matter halos of field galaxies that have been obtained from the most recent investigations of (i) weak galaxy-galaxy lensing and (ii) the dynamics of satellite galaxies in orbit about large host galaxies. Both of these techniques are statistical in their in their nature (i.e., large samples of galaxies are required to obtain a "signal"), but since they have inherently different selection biases and systematic errors they are quite complementary to each other. Results of work over the last several years on weak lensing and satellite dynamics is revealing a remarkably consistent picture regarding the dark matter halos of bright field galaxies (L > L*). The halos extend to large physical radii (> 150 kpc/h) and are flattened in projection on the sky, there is a marked difference in the depths of the potential wells of early-type galaxies and late-type galaxies, and the velocity dispersion profiles of the halos decrease at large projected radii. All of these are expected to hold true in a cold dark matter universe and, while neither technique can address the the possible small-scale conflicts between CDM and observed galaxies, on scales > 50 kpc/h both techniques yield results that are consistent with each other and with the predictions of CDM.Comment: 28 pages, 15 figures, invited review in "The New Cosmology", eds. R. E. Allen, D. V. Nanopoulos, and C. N. Pope, in pres

Satellite Galaxies in the Illustris-1 Simulation: Poor Tracers of the Mass Distribution

Number density profiles are computed for the satellites of relatively isolated host galaxies in the Illustris-1 simulation. The mean total mass density of the hosts is well-fitted by an NFW profile. The number density profile for the complete satellite sample is inconsistent with NFW and, on scales < 0.5 r_200, the satellites do not trace the hosts' mass. This differs substantially from previous results from semi-analytic galaxy formation models. The shape of the satellite number density profile depends on the luminosities of the hosts and the satellites, and on the host virial mass. The number density profile for the faintest satellites is well-fitted by an NFW profile, but the concentration is much less than the mean host mass density. The number density profile for the brightest satellites exhibits a steep increase in slope for host-satellite distances < 0.1 r_200, in qualitative agreement with recent observational studies that find a steep increase in the satellite number density at small host-satellite distances. On scales > 0.1 r_200 the satellites of the faintest hosts trace the host mass reasonably well. On scales > 0.4 r_200, the satellites of the brightest hosts do not trace the host mass and the satellite number density increases steeply for host-satellite distances < 0.1 r_200. The discrepancy between the satellite number density profile and the host mass density is most pronounced for the most massive systems, with the satellite number density falling far below that of the mass density on scales < 0.5 r_200.Comment: 11 pages, 4 figures, accepted for publication in ApJ Letter

The Spatial Distribution of Satellite Galaxies Selected from Redshift Space

We investigate the spatial distribution of satellite galaxies using a mock redshift survey of the first Millennium Run simulation. The satellites were identified using common redshift space criteria and the sample therefore includes a large percentage of interlopers. The satellite locations are well-fitted by a combination of a Navarro, Frenk & White(NFW) density profile and a power law. At fixed stellar mass, the NFW scale parameter, r_s, for the satellite distribution of red hosts exceeds r_s for the satellite distribution of blue hosts. In both cases the dependence of r_s on host stellar mass is well-fitted by a power law. For the satellites of red hosts, r_s^{red} \propto (M_\ast / M_\sun)^{0.71 \pm 0.05} while for the satellites of blue hosts, r_s^{blue} \propto (M_\ast / M_\sun)^{0.48 \pm 0.07}$. For hosts with stellar masses greater than 4.0E+10 M_sun, the satellite distribution around blue hosts is more concentrated than is the satellite distribution around red hosts. The spatial distribution of the satellites of red hosts traces that of the hosts' halos; however, the spatial distribution of the satellites of blue hosts is more concentrated than that of the hosts' halos by a factor of ~2. Our methodology is general and applies to any analysis of satellites in a mock redshift survey. However, our conclusions necessarily depend upon the semi-analytic galaxy formation model that was adopted, and different galaxy formation models may yield different results.Comment: 25 pages, 5 figures, accepted for publication in The Astrophysical Journa

Locations of Satellite Galaxies in the Two-Degree Field Galaxy Redshift Survey

We compute the locations of satellite galaxies in the Two-Degree Field Galaxy Redshift Survey using two sets of selection criteria and three sources of photometric data. Using the SuperCOSMOS r_F photometry, we find that the satellites are located preferentially near the major axes of their hosts, and the anisotropy is detected at a highly-significant level (confidence levels of 99.6% to 99.9%). The locations of satellites that have high velocities relative to their hosts are statistically indistinguishable from the locations of satellites that have low velocities relative to their hosts. Additionally, satellites with passive star formation are distributed anisotropically about their hosts (99% confidence level), while the locations of star-forming satellites are consistent with an isotropic distribution. These two distributions are, however, statistically indistinguishable. Therefore it is not correct to interpret this as evidence that the locations of the star-forming satellites are intrinsically different from those of the passive satellites.Comment: 21 pages, 3 figure

Satellite galaxies in the Illustris-1 simulation: anisotropic locations around relatively isolated hosts

We investigate the locations of satellite galaxies in the z = 0 redshift slice of the hydrodynamical Illustris-1 simulation. As expected from previous work, the satellites are distributed anisotropically in the plane of the sky, with a preference for being located near the major axes of their hosts. Due to misalignment of mass and light within the hosts, the degree of anisotropy is considerably less when satellite locations are measured with respect to the hosts' stellar surface mass density than when they are measured with respect to the hosts' dark matter surface mass density. When measured with respect to the hosts' dark matter surface mass density, the mean satellite location depends strongly on host stellar mass and luminosity, with the satellites of the faintest, least massive hosts showing the greatest anisotropy. When measured with respect to the hosts' stellar surface mass density, the mean satellite location is essentially independent of host stellar mass and luminosity. In addition, the satellite locations are largely insensitive to the amount of stellar mass used to define the hosts' stellar surface mass density, as long as at least 50% to 70% of the hosts' total stellar mass is used. The satellite locations are dependent upon the stellar masses of the satellites, with the most massive satellites having the most anisotropic distributions.Comment: 12 pages, 10 figures, accepted for publication in MNRA

The spatial correlation properties of dark galaxy halos in a CDM universe

We use the Hierarchical Particle Mesh (HPM) N-body code written by J. V. Villumsen (Villumsen, 1989) to investigate the two-point spatial correlation function, xi(r), of dark galaxy halos as a function of halo mass and local environment (i.e. high, low, or average mass density). We assume a standard cold dark matter (CDM) universe (omega = 1, delta = 0, H sub 0 = 50,km/sec/Mpc). Because of the large dynamic ranges in mass and length that can be obtained with the HPM code, it is well-suited to an investigation of this sort

Fast generation of large-scale structure density maps via generative adversarial networks

Generative Adversarial Networks (GANs) are a recent advancement in unsupervised machine learning. They are a cat-and-mouse game between two neural networks: (1) a discriminator network which learns to validate whether a sample is real or fake compared to a training set and (2) a generator network which learns to generate data that appear to belong to the training set. Both networks learn from each other until training is complete and the generator network is able to produce samples that are indistinguishable from the training set. We find that GANs are well-suited for fast generation of novel 3D density maps that are indistinguishable from those obtained from N-body simulations. In a matter of seconds, a fully trained GAN can generate thousands of density maps at different epochs in the history of the universe. These GAN-generated maps can then be used to study the evolution of large-scale structure over time.Accepted manuscrip