Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe SBF Survey of Galaxy Distances. II. Local and Large-Scale Flows
52
0
0.0
(
0
)
Authors
John L. Tonry
Ask ChatGPT about the research
No Arabic abstract
We present analysis of local large scale flows using the Surface Brightness Fluctuation (SBF) Survey for the distances to 300 early-type galaxies. Our models of the distribution function of mean velocity and velocity dispersion at each point in space include a uniform thermal velocity dispersion and spherical attractors whose position, amplitude, and radial shape are free to vary. Our fitting procedure performs a maximum likelihood fit of the model to the observations. We obtain a Hubble constant of Ho = 77 +/- 4 +/- 7 km/s/Mpc, but a uniform Hubble flow is not acceptable fit to the data. Inclusion of two attractors, one of whose fit location coincides with the Virgo cluster and the other whose fit location is slightly beyond the Centaurus clusters nearly explain the peculiar velocities, but the quality of the fit can be further improved by the addition of a quadrupole correction to the Hubble flow. Although the dipole and quadrupole may be genuine manifestations of more distant density fluctuations, we find evidence that they are more likely due to non-spherical attractors. We find no evidence for bulk flows which include our entire survey volume (R < 3000 km/s); our volume is at rest with respect to the CMB. The fits to the attractors both have isothermal radial profiles (v ~ 1/r) over a range of overdensity between about 10 and 1, but fall off more steeply at larger radius. The best fit value for the small scale, cosmic thermal velocity is 180 +/- 14 km/s.
rate research
Read More
We report data for $I$ band Surface Brightness Fluctuation (SBF) magnitudes, V-I colors, and distance moduli for 300 galaxies. The Survey contains E, S0 and early-type spiral galaxies in the proportions of 49:42:9, and is essentially complete for E galaxies to Hubble velocities of 2000 km/s, with a substantial sampling of E galaxies out to 4000 km/s. The median error in distance modulus is 0.22 mag. We also present two new results from the Survey. (1) We compare the mean peculiar flow velocity (bulk flow) implied by our distances with predictions of typical cold dark matter transfer functions as a function of scale, and find very good agreement with cold, dark matter cosmologies if the transfer function scale parameter $Gamma$, and the power spectrum normalization $sigma_8$ are related by $sigma_8 Gamma^{-0.5} approx 2pm0.5$. Derived directly from velocities, this result is independent of the distribution of galaxies or models for biasing. The modest bulk flow contradicts reports of large-scale, large-amplitude flows in the $sim200$ Mpc diameter volume surrounding our Survey volume. (2) We present a distance-independent measure of absolute galaxy luminosity, Nbar, and show how it correlates with galaxy properties such as color and velocity dispersion, demonstrating its utility for measuring galaxy distances through large and unknown extinction.
The Large Scale Structure (LSS) in the galaxy distribution is investigated using the Sloan Digital Sky Survey Early Data Release (SDSS EDR). Using the Minimal Spanning Tree technique we have extracted sets of filaments, of wall-like structures, of galaxy groups, and of rich clusters from this unique sample. The physical properties of these structures were then measured and compared with the expectations from Zeldovich theory. The measured characteristics of galaxy walls were found to be consistent with those for a spatially flat $Lambda$CDM cosmological model with $Omega_mapprox$ 0.3 and $Omega_Lambda approx$ 0.7, and for Gaussian initial perturbations with a Harrison -- Zeldovich power spectrum. Furthermore, we found that the mass functions of groups and of unrelaxed structure elements generally fit well with the expectations from Zeldovich theory, although there was some discrepancy for lower mass groups which may be due to incompleteness in the selected sample of groups. We also note that both groups and rich clusters tend to prefer the environments of walls, which tend to be of higher density, rather than the environments of filaments, which tend to be of lower density. Finally, we note evidence of systematic differences in the properties of the LSS between the Northern Galactic Cap stripe and the Southern Galactic Cap stripe -- in particular, in the physical properties of the walls, their spatial distribution, and the relative numbers of clusters embedded in walls. Because the mean separation of walls is $approx$ 60 -- 70$h^{-1}$ Mpc, each stripe only intersects a few tens of walls. Thus, small number statistics and cosmic variance are the likely drivers of these systematic differences.
Reconstruction of the local velocity field from the overdensity field and a gravitational acceleration that falls off from a point mass as r^-2 yields velocities in broad agreement with peculiar velocities measured with galaxy distance indicators. MONDian gravity does not. To quantify this, we introduce the velocity angular correlation function as a diagnostic of peculiar velocity field alignment and coherence as a function of scale. It is independent of the bias parameter of structure formation in the standard model of cosmology and the acceleration parameter of MOND. A modified gravity acceleration consistent with observed large scale structure would need to asymptote to zero at large distances more like r^-2, than r^-1.
In the recent papers, we introduced a method utilised to measure the flow field. The method is based on the tracking of supergranular structures. We did not precisely know, whether its results represent the flow field in the photosphere or in some sub-photospheric layers. In this paper, in combination with helioseismic data, we are able to estimate the depths in the solar convection envelope, where the detected large-scale flow field is well represented by the surface measurements. We got a clear answer to question what kind of structures we track in full-disc Dopplergrams. It seems that in the quiet Sun regions the supergranular structures are tracked, while in the regions with the magnetic field the structures of the magnetic field are dominant. This observation seems obvious, because the nature of Doppler structures is different in the magnetic regions and in the quiet Sun. We show that the large-scale flow detected by our method represents the motion of plasma in layers down to ~10 Mm. The supergranules may therefore be treated as the objects carried by the underlying large-scale velocity field.
Wasserstein gradient flows provide a powerful means of understanding and solving many diffusion equations. Specifically, Fokker-Planck equations, which model the diffusion of probability measures, can be understood as gradient descent over entropy functionals in Wasserstein space. This equivalence, introduced by Jordan, Kinderlehrer and Otto, inspired the so-called JKO scheme to approximate these diffusion processes via an implicit discretization of the gradient flow in Wasserstein space. Solving the optimization problem associated to each JKO step, however, presents serious computational challenges. We introduce a scalable method to approximate Wasserstein gradient flows, targeted to machine learning applications. Our approach relies on input-convex neural networks (ICNNs) to discretize the JKO steps, which can be optimized by stochastic gradient descent. Unlike previous work, our method does not require domain discretization or particle simulation. As a result, we can sample from the measure at each time step of the diffusion and compute its probability density. We demonstrate our algorithms performance by computing diffusions following the Fokker-Planck equation and apply it to unnormalized density sampling as well as nonlinear filtering.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
