No Arabic abstract
There are hints suggesting that properties of galaxy populations in dark matter haloes may depend on their large-scale environment. Recent works point out that very low-density environments influence halo occupation distribution (HOD), however there is not a similar analysis focused on high-density environments. Here we use a simulated set of future virialized superstructures (FVS) to analyse the occupation of galaxies in haloes within these high globally dense regions. We use a publicly available simulated galaxy set constructed with a semi-analytical model to identify FVS in the simulation. Then, we computed the HOD within these superstructures for different absolute magnitude thresholds and make several analysis including the comparison to the global HOD results. We study the dependence on the results on properties of the FVS such as density and volume as well as consider the morphology of galaxies. We also analysed the properties of the stellar content of galaxies and the formation time of the haloes inside FVS. We find a significant increase in the HOD inside FVS. This result is present for all absolute magnitude thresholds explored. The effect is larger in the densest regions of FVS, but does not depend on the volume of the superstructure. We also find that the stellar-mass content of galaxies considerably differs inside the superstructures. Low mass haloes have their central and satellite galaxies with a higher stellar mass content (50%), and exhibit mean star ages (20%) older than average. For massive haloes in FVS we find that only the stellar mass of satellite galaxies varies considerably corresponding to a decrease of 50%. We find a significant statistical difference between the formation times of haloes in FVS and the average population. Haloes residing in superstructures formed earlier, a fact that leads to several changes in the HOD and their member galaxy properties.
We present a clustering analysis of near ultraviolet (NUV) - optical color selected luminosity bin samples of green valley galaxies. These galaxy samples are constructed by matching the Sloan Digital Sky Survey Data Release 7 with the latest Galaxy Evolution Explorer source catalog which provides NUV photometry. We present cross-correlation function measurements and determine the halo occupation distribution of these transitional galaxies using a new multiple tracer analysis technique. We extend the halo-occupation formalism to model the cross-correlation function between a galaxy sample of interest and multiple tracer populations simultaneously. This method can be applied to commonly used luminosity threshold samples as well as to color and luminosity bin selected galaxy samples, and improves the accuracy of clustering analyses for sparse galaxy populations. We confirm the previously observed trend that red galaxies reside in more massive halos and are more likely to be satellite galaxies than average galaxies of similar luminosity. While the change in central galaxy host mass as a function of color is only weakly constrained, the satellite fraction and characteristic halo masses of green satellite galaxies are found to be intermediate between those of blue and red satellite galaxies.
We present a clustering analysis of ~60,000 massive (stellar mass Mstar > 10^{11} Msun) galaxies out to z = 1 drawn from 55.2 deg2 of the UKIRT Infrared Deep Sky Survey (UKIDSS) and the Sloan Digital Sky Survey (SDSS) II Supernova Survey. Strong clustering is detected for all the subsamples of massive galaxies characterized by different stellar masses (Mstar = 10^{11.0-11.5} Msun, 10^{11.5-12.0} Msun) or rest-frame colors (blue: U - V < 1.0, red: U - V > 1.0). We find that more mature (more massive or redder) galaxies are more clustered, which implies that more mature galaxies have started stellar-mass assembly earlier within the highly-biased region where the structure formation has also started earlier. By means of halo occupation distribution (HOD) models fitted to the observed angular correlation function, we infer the properties of the underlying host dark halos. We find that the estimated bias factors and host halo masses are systematically larger for galaxies with larger stellar masses, which is consistent with the general agreement that the capability of hosting massive galaxies depends strongly on halo mass. The estimated effective halo masses are ~10^{14} Msun, which gives the stellar-mass to halo-mass ratios of ~0.003. The observed evolution of bias factors indicates rapid evolution of spatial distributions of cold dark matter relative to those traced by the massive galaxies, while the transition of host halo masses might imply that the fractional mass growth rate of halos is less than those of stellar systems. The inferred halo masses and high fractions of central galaxies indicate that the massive galaxies in the current sample are possibly equivalent to central galaxies of galaxy clusters.
We find the distribution function f(E) for dark matter (DM) halos in galaxies and the corresponding equation of state from the (empirical) DM density profiles derived from observations. We solve for DM in galaxies the analogous of the Eddington equation originally used for the gas of stars in globular clusters. The observed density profiles are a good realistic starting point and the distribution functions derived from them are realistic. We do not make any assumption about the DM nature, the methods developed here apply to any DM kind, though all results are consistent with Warm DM. With these methods we find: (i) Cored density profiles behaving quadratically for small distances rho(r) r -> 0 = rho(0) - K r^2 produce distribution functions which are finite and positive at the halo center while cusped density profiles always produce divergent distribution functions at the center. (ii) Cored density profiles produce approximate thermal Boltzmann distribution functions for r < 3 r_h where r_h is the halo radius. (iii) Analytic expressions for the dispersion velocity and the pressure are derived yielding an ideal DM gas equation of state with local temperature T(r) = m v^2(r)/3. T(r) turns to be constant in the same region where the distribution function is thermal and exhibits the same temperature within the percent. The self-gravitating DM gas can thermalize despite being collisionless because it is an ergodic system. (iv) The DM halo can be consistently considered at local thermal equilibrium with: (a) a constant temperature T(r) = T_0 for r < 3 ; r_h, (b) a space dependent temperature T(r) for 3 r_h < r < R_{virial}, which slowly decreases with r. That is, the DM halo is realistically a collisionless self-gravitating thermal gas for r < R_{virial}. (v) T(r) outside the halo radius nicely follows the decrease of the circular velocity squared.
New photometric and long-slit spectroscopic observations are presented for NGC 7113, PGC 1852, and PGC 67207 which are three bright galaxies residing in low-density environments. The surface-brightness distribution is analysed from the K_S-band images taken with adaptive optics at the Gemini North Telescope and the ugriz-band images from the Sloan Digital Sky Survey while the line-of-sight stellar velocity distribution and line-strength Lick indices inside the effective radius are measured along several position angles. The age, metallicity, and alpha-element abundance of the galaxies are estimated from single stellar-population models. In spite of the available morphological classification, images show that PGC 1852 is a barred spiral which we do not further consider for mass modelling. The structural parameters of the two early-type galaxies NGC 7113 and PGC 67207 are obtained from a two-dimensional photometric decomposition and the mass-to-light ratio of all the (luminous and dark) mass that follows the light is derived from orbit-based axisymmetric dynamical modelling together with the mass density of the dark matter halo. The dynamically derived mass that follows the light is about a factor of 2 larger than the stellar mass derived using stellar-population models with Kroupa initial mass function. Both galaxies have a lower content of halo dark matter with respect to early-type galaxies in high-density environments and in agreement with the predictions of semi-analytical models of galaxy formation.
Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research. In this paper, we analyze the predictions made by a specific type of recurrent neural network, mixture density RNNs (MD-RNNs). These networks learn to model predictions as a combination of multiple Gaussian distributions, making them particularly interesting for problems where a sequence of inputs may lead to several distinct future possibilities. An example is learning internal models of an environment, where different events may or may not occur, but where the average over different events is not meaningful. By analyzing the predictions made by trained MD-RNNs, we find that their different Gaussian components have two complementary roles: 1) Separately modeling different stochastic events and 2) Separately modeling scenarios governed by different rules. These findings increase our understanding of what is learned by predictive MD-RNNs, and open up new research directions for further understanding how we can benefit from their self-organizing model decomposition.