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We study how well we can reconstruct the 2-point clustering of galaxies on linear scales, as a function of mass and luminosity, using the halo occupation distribution (HOD) in several semi-analytical models (SAMs) of galaxy formation from the Millennium Simulation. We find that HOD with Friends of Friends groups can reproduce galaxy clustering better than gravitationally bound haloes. This indicates that Friends of Friends groups are more directly related to the clustering of these regions than the bound particles of the overdensities. In general we find that the reconstruction works at best to 5% accuracy: it underestimates the bias for bright galaxies. This translates to an overestimation of 50% in the halo mass when we use clustering to calibrate mass. We also found a degeneracy on the mass prediction from the clustering amplitude that affects all the masses. This effect is due to the clustering dependence on the host halo substructure, an indication of assembly bias. We show that the clustering of haloes of a given mass increases with the number of subhaloes, a result that only depends on the underlying matter distribution. As the number of galaxies increases with the number of subhaloes in SAMs, this results in a low bias for the HOD reconstruction. We expect this effect to apply to other models of galaxy formation, including the real universe, as long as the number of galaxies incresases with the number of subhaloes. We have also found that the reconstructions of galaxy bias from the HOD model fails for low mass haloes with M = 3-5x10^11 Msun/h. We find that this is because galaxy clustering is more strongly affected by assembly bias for these low masses.
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Using dark matter simulations we show how halo bias is determined by local density and not by halo mass. This is not totally surprising, as according to the peak-background split model, local density is the property that constraints bias at large scales. Massive haloes have a high clustering because they reside in high density regions. Small haloes can be found in a wide range of environments which determine their clustering amplitudes differently. This contradicts the assumption of standard Halo Occupation Distribution (HOD) models that the bias and occupation of haloes is determined solely by their mass. We show that the bias of central galaxies from semi-analytic models of galaxy formation as a function of luminosity and colour is not correctly predicted by the standard HOD model. Using local density instead of halo mass the HOD model correctly predicts galaxy bias. These results indicate the need to include information about local density and not only mass in order to correctly apply HOD analysis in these galaxy samples. This new model can be readily applied to observations and has the advantage that the galaxy density can be directly observed, in contrast with the dark matter halo mass.
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.
We combine the most precise small scale ($< 100, rm h^{-1}kpc$) quasar clustering constraintsto date with recent measurements at large scales ($> 1, rm h^{-1}Mpc$) from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) to better constrain the satellite fraction of quasars at $zsim 1.5$ in the halo occupation formalism. We build our Halo Occupation Distribution (HOD) framework based on commonly used analytic forms for the one and two-halo terms with two free parameters: the minimum halo mass that hosts a central quasar and the fraction of satellite quasars that are within one halo. Inspired by recent studies that propose a steeper density profile for the dark matter haloes that host quasars, we explore HOD models at kiloparsec scales and best-fit parameters for models with $10times$ higher concentration parameter. We find that an HOD model with a satellite fraction of $f_{rm sat} = 0.071_{-0.004}^{+0.009}$ and minimum mass of $rm M_{m} = 2.31_{-0.38}^{+0.41} times 10^{12}, , rm h^{-1} M_{odot}$ for the host dark matter haloes best describes quasar clustering (on all scales) at $z sim 1.5$. Our results are marginally inconsistent with earlier work that studied brighter quasars, hinting at a luminosity-dependence to the one-halo term.
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 use the projected correlation function w_p(r_p) of a volume-limited subsample of the Sloan Digital Sky Survey (SDSS) main galaxy redshift catalogue to measure the halo occupation distribution (HOD) of the galaxies of the sample. Simultaneously, we allow the cosmology to vary within cosmological constraints imposed by cosmic microwave background experiments in a Lambda-CDM model. We find that combining w_p(r_p) for this sample alone with the observations by WMAP, ACBAR, CBI and VSA can provide one of the most precise techniques available to measure cosmological parameters. For a minimal flat six-parameter Lambda-CDM model with an HOD with three free parameters, we find Omega_m=0.278^{+0.027}_{-0.027}, sigma_8=0.812^{+0.028}_{-0.027}, and H_0=69.8^{+2.6}_{-2.6}km s^{-1} Mpc^{-1}; these errors are significantly smaller than from CMB alone and similar to those obtained by combining CMB with the large-scale galaxy power spectrum assuming scale-independent bias. The corresponding HOD parameters describing the minimum halo mass and the normalization and cut-off of the satellite mean occupation are M_min=(3.03^{+0.36}_{-0.36})x 10^{12} h^{-1} M_sun, M_1 = (4.58^{+0.60}_{-0.60})x 10^{13} h^{-1} M_sun, and kappa=4.44^{+0.51}_{-0.69}. When more parameters are added to the HOD model, the error bars on the HOD parameters increase because of degeneracies, but the error bars on the cosmological parameters do not increase greatly. Similar modeling for other galaxy samples could reduce the statistical errors on these results, while more thorough investigations of the cosmology dependence of nonlinear halo bias and halo mass functions are needed to eliminate remaining systematic uncertainties, which may be comparable to statistical uncertainties.
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