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Register a new userAssembly bias in quadratic bias parameters of dark matter halos from forward modeling
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We use the forward modeling approach to galaxy clustering combined with the likelihood from the effective-field theory of large-scale structure to measure assembly bias, i.e. the dependence of halo bias on properties beyond the total mass, in the linear ($b_1$) and second order bias parameters ($b_2$ and $b_{K^2}$) of dark matter halos in $N$-body simulations. This is the first time that assembly bias in the tidal bias parameter $b_{K^2}$ is measured. We focus on three standard halo properties: the concentration $c$, spin $lambda$, and sphericity $s$, for which we find an assembly bias signal in $b_{K^2}$ that is opposite to that in $b_1$. Specifically, at fixed mass, halos that get more (less) positively biased in $b_1$, get less (more) negatively biased in $b_{K^2}$. We also investigate the impact of assembly bias on the $b_2(b_1)$ and $b_{K^2}(b_1)$ relations, and find that while the $b_2(b_1)$ relation stays roughly unchanged, assembly bias strongly impacts the $b_{K^2}(b_1)$ relation. This impact likely extends also to the corresponding relation for galaxies, which motivates future studies to design better priors on $b_{K^2}(b_1)$ for use in cosmological constraints from galaxy clustering data.
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We study the dependence of the galaxy content of dark matter halos on large-scale environment and halo formation time using semi-analytic galaxy models applied to the Millennium simulation. We analyze subsamples of halos at the extremes of these distributions and measure the occupation functions for the galaxies they host. We find distinct differences in these occupation functions. The main effect with environment is that central galaxies (and in one model also the satellites) in denser regions start populating lower-mass halos. A similar, but significantly stronger, trend exists with halo age, where early-forming halos are more likely to host central galaxies at lower halo mass. We discuss the origin of these trends and the connection to the stellar mass -- halo mass relation. We find that, at fixed halo mass, older halos and to some extent also halos in dense environments tend to host more massive galaxies. Additionally, we see a reverse trend for the satellite galaxies occupation where early-forming halos have fewer satellites, likely due to having more time for them to merge with the central galaxy. We describe these occupancy variations also in terms of the changes in the occupation function parameters, which can aid in constructing realistic mock galaxy catalogs. Finally, we study the corresponding galaxy auto- and cross-correlation functions of the different samples and elucidate the impact of assembly bias on galaxy clustering. Our results can inform theoretical models of assembly bias and attempts to detect it in the real universe.
We explore the phenomenon commonly known as halo assembly bias, whereby dark matter halos of the same mass are found to be more or less clustered when a second halo property is considered, for halos in the mass range $3.7 times 10^{11} ; h^{-1} mathrm{M_{odot}} - 5.0 times 10^{13} ; h^{-1} mathrm{M_{odot}}$. Using the Large Suite of Dark Matter Simulations (LasDamas) we consider nine commonly used halo properties and find that a clustering bias exists if halos are binned by mass or by any other halo property. This secondary bias implies that no single halo property encompasses all the spatial clustering information of the halo population. The mean values of some halo properties depend on their halos distance to a more massive neighbor. Halo samples selected by having high values of one of these properties therefore inherit a neighbor bias such that they are much more likely to be close to a much more massive neighbor. This neighbor bias largely accounts for the secondary bias seen in halos binned by mass and split by concentration or age. However, halos binned by other mass-like properties still show a secondary bias even when the neighbor bias is removed. The secondary bias of halos selected by their spin behaves differently than that for other halo properties, suggesting that the origin of the spin bias is different than of other secondary biases.
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over unprecedented volumes. Mock catalogues are needed to fully exploit the potential of these surveys. Standard techniques employed to create these mock catalogs, like Halo Occupation Distribution (HOD), rely on assumptions such as the baryonic properties of dark matter halos only depend on their masses. In this work, we use the state-of-the-art magneto-hydrodynamic simulation IllustrisTNG to show that the HI content of halos exhibits a strong dependence on their local environment. We then use machine learning techniques to show that this effect can be 1) modeled by these algorithms and 2) parametrized in the form of novel analytic equations. We provide physical explanations for this environmental effect and show that ignoring it leads to underprediction of the real-space 21-cm power spectrum at $kgtrsim 0.05$ h/Mpc by $gtrsim$10%, which is larger than the expected precision from upcoming surveys on such large scales. Our methodology of combining numerical simulations with machine learning techniques is general, and opens a new direction at modeling and parametrizing the complex physics of assembly bias needed to generate accurate mocks for galaxy and line intensity mapping surveys.
We use field-level forward models of galaxy clustering and the EFT likelihood formalism to study, for the first time for self-consistently simulated galaxies, the relations between the linear $b_1$ and second-order bias parameters $b_2$ and $b_{K^2}$. The forward models utilize all of the information available in the galaxy distribution up to a given order in perturbation theory, which allows us to infer these bias parameters with high signal-to-noise, even from relatively small volumes ($L_{rm box} = 205{rm Mpc}/h$). We consider galaxies from the IllustrisTNG simulations, and our main result is that the $b_2(b_1)$ and $b_{K^2}(b_1)$ relations obtained from gravity-only simulations for total mass selected objects are broadly preserved for simulated galaxies selected by stellar mass, star formation rate, color and black hole accretion rate. We also find good agreement between the bias relations of the simulated galaxies and a number of recent estimates for observed galaxy samples. The consistency under different galaxy selection criteria suggests that theoretical priors on these bias relations may be used to improve cosmological constraints based on observed galaxy samples. We do identify some small differences between the bias relations in the hydrodynamical and gravity-only simulations, which we show can be linked to the environmental dependence of the relation between galaxy properties and mass. We also show that the EFT likelihood recovers the value of $sigma_8$ to percent-level from various galaxy samples (including splits by color and star formation rate) and after marginalizing over 8 bias parameters. This demonstration using simulated galaxies adds to previous works based on halos as tracers, and strengthens further the potential of forward models to infer cosmology from galaxy data.
Dark matter halo clustering depends not only on halo mass, but also on other properties such as concentration and shape. This phenomenon is known broadly as assembly bias. We explore the dependence of assembly bias on halo definition, parametrized by spherical overdensity parameter, $Delta$. We summarize the strength of concentration-, shape-, and spin-dependent halo clustering as a function of halo mass and halo definition. Concentration-dependent clustering depends strongly on mass at all $Delta$. For conventional halo definitions ($Delta sim 200mathrm{m}-600mathrm{m}$), concentration-dependent clustering at low mass is driven by a population of haloes that is altered through interactions with neighbouring haloes. Concentration-dependent clustering can be greatly reduced through a mass-dependent halo definition with $Delta sim 20mathrm{m}-40mathrm{m}$ for haloes with $M_{200mathrm{m}} lesssim 10^{12}, h^{-1}mathrm{M}_{odot}$. Smaller $Delta$ implies larger radii and mitigates assembly bias at low mass by subsuming altered, so-called backsplash haloes into now larger host haloes. At higher masses ($M_{200mathrm{m}} gtrsim 10^{13}, h^{-1}mathrm{M}_{odot}$) larger overdensities, $Delta gtrsim 600mathrm{m}$, are necessary. Shape- and spin-dependent clustering are significant for all halo definitions that we explore and exhibit a relatively weaker mass dependence. Generally, both the strength and the sense of assembly bias depend on halo definition, varying significantly even among common definitions. We identify no halo definition that mitigates all manifestations of assembly bias. A halo definition that mitigates assembly bias based on one halo property (e.g., concentration) must be mass dependent. The halo definitions that best mitigate concentration-dependent halo clustering do not coincide with the expected average splashback radii at fixed halo mass.
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