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Register a new userThe cosmological dependence of halo and galaxy assembly bias
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One of the main predictions of excursion set theory is that the clustering of dark matter haloes only depends on halo mass. However, it has been long established that the clustering of haloes also depends on other properties, including formation time, concentration, and spin; this effect is commonly known as halo assembly bias. We use a suite of gravity-only simulations to study the dependence of halo assembly bias on cosmology; these simulations cover cosmological parameters spanning 10$sigma$ around state-of-the-art best-fitting values, including standard extensions of the $Lambda$CDM paradigm such as neutrino mass and dynamical dark energy. We find that the strength of halo assembly bias presents variations smaller than 0.05 dex across all cosmologies studied for concentration and spin selected haloes, letting us conclude that the dependence of halo assembly bias upon cosmology is negligible. We then study the dependence of galaxy assembly bias (i.e. the manifestation of halo assembly bias in galaxy clustering) on cosmology using subhalo abundance matching. We find that galaxy assembly bias also presents very small dependence upon cosmology ($sim$ 2$%$-4$%$ of the total clustering); on the other hand, we find that the dependence of this signal on the galaxy formation parameters of our galaxy model is much stronger. Taken together, these results let us conclude that the dependence of halo and galaxy assembly bias on cosmology is practically negligible.
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Empirical methods for connecting galaxies to their dark matter halos have become essential for interpreting measurements of the spatial statistics of galaxies. In this work, we present a novel approach for parameterizing the degree of concentration dependence in the abundance matching method. This new parameterization provides a smooth interpolation between two commonly used matching proxies: the peak halo mass and the peak halo maximal circular velocity. This parameterization controls the amount of dependence of galaxy luminosity on halo concentration at a fixed halo mass. Effectively this interpolation scheme enables abundance matching models to have adjustable assembly bias in the resulting galaxy catalogs. With the new 400 Mpc/h DarkSky Simulation, whose larger volume provides lower sample variance, we further show that low-redshift two-point clustering and satellite fraction measurements from SDSS can already provide a joint constraint on this concentration dependence and the scatter within the abundance matching framework.
We present evidence for halo assembly bias as a function of geometric environment. By classifying GAMA galaxy groups as residing in voids, sheets, filaments or knots using a tidal tensor method, we find that low-mass haloes that reside in knots are older than haloes of the same mass that reside in voids. This result provides direct support to theories that link strong halo tidal interactions with halo assembly times. The trend with geometric environment is reversed at large halo mass, with haloes in knots being younger than haloes of the same mass in voids. We find a clear signal of halo downsizing - more massive haloes host galaxies that assembled their stars earlier. This overall trend holds independently of geometric environment. We support our analysis with an in-depth exploration of the L-Galaxies semi-analytic model, used here to correlate several galaxy properties with three different definitions of halo formation time. We find a complex relationship between halo formation time and galaxy properties, with significant scatter. We confirm that stellar mass to halo mass ratio, specific star-formation rate and mass-weighed age are reasonable proxies of halo formation time, especially at low halo masses. Instantaneous star-formation rate is a poor indicator at all halo masses. Using the same semi-analytic model, we create mock spectral observations using complex star-formation and chemical enrichment histories, that approximately mimic GAMAs typical signal-to-noise and wavelength range. We use these mocks to assert how well potential proxies of halo formation time may be recovered from GAMA-like spectroscopic data.
The two-point clustering of dark matter halos is influenced by halo properties besides mass, a phenomenon referred to as halo assembly bias. Using the depth of the gravitational potential well, $V_{rm max}$, as our secondary halo property, in this paper we present the first study of the scale-dependence assembly bias. In the large-scale linear regime, $rgeq10h^{-1}{rm Mpc},$ our findings are in keeping with previous results. In particular, at the low-mass end ($M_{rm vir}<M_{rm coll}approx10^{12.5}{rm M}_{odot}$), halos with high-$V_{rm max}$ show stronger large-scale clustering relative to halos with low-$V_{rm max}$ of the same mass, this trend weakens and reverses for $M_{rm vir}geq M_{rm coll}.$ In the nonlinear regime, assembly bias in low-mass halos exhibits a pronounced scale-dependent bump at $500h^{-1}{rm kpc}-5h^{-1}{rm Mpc},$ a new result. This feature weakens and eventually vanishes for halos of higher mass. We show that this scale-dependent signature can primarily be attributed to a special subpopulation of ejected halos, defined as present-day host halos that were previously members of a higher-mass halo at some point in their past history. A corollary of our results is that galaxy clustering on scales of $rsim1-2h^{-1}{rm Mpc}$ can be impacted by up to $sim15%$ by the choice of the halo property used in the halo model, even for stellar mass-limited samples.
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.
The simplest analyses of halo bias assume that halo mass alone determines halo clustering. However, if the large scale environment is fixed, then halo clustering is almost entirely determined by environment, and is almost completely independent of halo mass. We show why. Our analysis is useful for studies which use the environmental dependence of clustering to constrain cosmological and galaxy formation models. It also shows why many correlations between galaxy properties and environment are merely consequences of the underlying correlations between halos and their environments, and provides a framework for quantifying such inherited correlations.
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