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Register a new userHerMES: Halo Occupation Number and Bias Properties of Dusty Galaxies from Angular Clustering Measurements
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We measure the angular correlation function, w(theta), from 0.5 to 30 arcminutes of detected sources in two wide fields of the Herschel Multi-tiered Extragalactic Survey (HerMES). Our measurements are consistent with the expected clustering shape from a population of sources that trace the dark matter density field, including non-linear clustering at arcminute angular scales arising from multiple sources that occupy the same dark matter halos. By making use of the halo model to connect the spatial clustering of sources to the dark matter halo distribution, we estimate source bias and halo occupation number for dusty sub-mm galaxies at z ~ 2. We find that sub-mm galaxies with 250 micron flux densities above 30 mJy reside in dark matter halos with mass above (5pm4) x 10^12 M_sun, while (14pm8)% of such sources appear as satellites in more massive halos.
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We present measurements of the auto- and cross-frequency power spectra of the cosmic infrared background (CIB) at 250, 350, and 500um (1200, 860, and 600 GHz) from observations totaling ~ 70 deg^2 made with the SPIRE instrument aboard the Herschel Space Observatory. We measure a fractional anisotropy dI / I = 14 +- 4%, detecting signatures arising from the clustering of dusty star-forming galaxies in both the linear (2-halo) and non-linear (1-halo) regimes; and that the transition from the 2- to 1-halo terms, below which power originates predominantly from multiple galaxies within dark matter halos, occurs at k_theta ~ 0.1 - 0.12 arcmin^-1 (l ~ 2160 - 2380), from 250 to 500um. New to this paper is clear evidence of a dependence of the Poisson and 1-halo power on the flux-cut level of masked sources --- suggesting that some fraction of the more luminous sources occupy more massive halos as satellites, or are possibly close pairs. We measure the cross-correlation power spectra between bands, finding that bands which are farthest apart are the least correlated, as well as hints of a reduction in the correlation between bands when resolved sources are more aggressively masked. In the second part of the paper we attempt to interpret the measurements in the framework of the halo model. With the aim of fitting simultaneously with one model the power spectra, number counts, and absolute CIB level in all bands, we find that this is achievable by invoking a luminosity-mass relationship, such that the luminosity-to-mass ratio peaks at a particular halo mass scale and declines towards lower and higher mass halos. Our best-fit model finds that the halo mass which is most efficient at hosting star formation in the redshift range of peak star-forming activity, z ~ 1-3, is log(M_peak/M_sun) ~ 12.1 +- 0.5, and that the minimum halo mass to host infrared galaxies is log(M_min/M_sun) ~ 10.1 +- 0.6.
We use a large dark matter simulation of a LambdaCDM model to investigate the clustering and environmental dependence of the number of substructures in a halo. Focusing on redshift z=1, we find that the halo occupation distribution is sensitive at the tens of percent level to the surrounding density and to a lesser extent to asymmetry of the surrounding density distribution. We compute the autocorrelation function of halos as a function of occupation, building on the finding of Wechsler et al. (2006) and Gao and White (2007) that halos (at fixed mass) with more substructure are more clustered. We compute the relative bias as a function of occupation number at fixed mass, finding a strong relationship. At fixed mass, halos in the top 5% of occupation can have an autocorrelation function ~ 1.5-2 times higher than the mean. We also compute the bias as a function of halo mass, for fixed halo occupation. We find that for group and cluster sized halos, when the number of subhalos is held fixed, there is a strong anticorrelation between bias and halo mass. Such a relationship represents an additional challenge to the halo model.
The potential for Planck to detect clusters of dusty, star-forming galaxies at z greater than 1 is tested by examining the Herschel-SPIRE images of Planck Early Release Compact Source Catalog (ERCSC) sources lying in fields observed by the HerMES survey. Of the 16 Planck sources that lie in the roughly 90 sq. deg. examined, we find that twelve are associated with single bright Herschel sources. The remaining four are associated with overdensities of Herschel sources, making them candidate clusters of dusty, starforming galaxies. We use complementary optical and NIR data for these clumps to test this idea, and find evidence for the presence of galaxy clusters in all four cases. We use photometric redshifts and red sequence galaxies to estimate the redshifts of these clusters, finding that they range from 0.8 to 2.3. These redshifts imply that the Herschel sources in these clusters, which contribute to the detected Planck flux, are forming stars very rapidly, with typical total cluster star formation rates greater than 1000Msun per yr. The high redshift clusters discovered in these observations are used to constrain the epoch of cluster galaxy formation, finding that the galaxies in our clusters are 1 to 1.5 Gy old at z about 1 to 2. Prospects for the discovery of further clusters of dusty galaxies are discussed, using not only all sky Planck surveys, but also deeper, smaller area, Herschel surveys.
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the halo occupations and recover galaxy clustering and assembly bias in a semi-analytic galaxy formation model. For stellar-mass selected samples, we train a Random Forest algorithm on the number of central and satellite galaxies in each dark matter halo. With the predicted occupations, we create mock galaxy catalogues and measure the clustering and assembly bias. Using a range of halo and environment properties, we find that the machine learning predictions of the occupancy variations with secondary properties, galaxy clustering and assembly bias are all in excellent agreement with those of our target galaxy formation model. Internal halo properties are most important for the central galaxies prediction, while environment plays a critical role for the satellites. Our machine learning models are all provided in a usable format. We demonstrate that machine learning is a powerful tool for modelling the galaxy-halo connection, and can be used to create realistic mock galaxy catalogues which accurately recover the expected occupancy variations, galaxy clustering and galaxy assembly bias, imperative for cosmological analyses of upcoming surveys.
We present the clustering properties and halo occupation distribution (HOD) modelling of very low redshift, hard X-ray-detected active galactic nuclei (AGN) using cross-correlation function measurements with Two-Micron All Sky Survey galaxies. Spanning a redshift range of $0.007 < z < 0.037$, with a median $z=0.024$, we present a precise AGN clustering study of the most local AGN in the Universe. The AGN sample is drawn from the SWIFT/BAT 70-month and INTEGRAL/IBIS eight year all-sky X-ray surveys and contains both type I and type II AGN. We find a large-scale bias for the full AGN sample of $b=1.04^{+0.10}_{-0.11}$, which corresponds to a typical host dark matter halo mass of $M_{rm h}^{rm typ}=12.84^{+0.22}_{-0.30},h^{-1} M_{odot}$. When split into low and high X-ray luminosity and type I and type II AGN subsamples, we detect no statistically significant differences in the large-scale bias parameters. However, there are differences in the small-scale clustering which are reflected in the full HOD model results. We find that low and high X-ray luminosity AGN, as well as type I and type II AGN, occupy dark matter haloes differently, with 3.4$sigma$ and 4.0$sigma$ differences in their mean halo masses, respectively, when split by luminosity and type. The latter finding contradicts a simple orientation-based AGN unification model. As a by-product of our cross-correlation approach, we also present the first HOD model of 2MASS galaxies.
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