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Register a new userPredicting halo occupation and galaxy assembly bias with machine learning
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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.
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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$10%, which is larger than the expected precision from upcoming surveys on such large scales. Our methodology of combining numerical simulations with machine learning techniques is general, and opens a new direction at modeling and parametrizing the complex physics of assembly bias needed to generate accurate mocks for galaxy and line intensity mapping surveys.
One of the main predictions of excursion set theory is that the clustering of dark matter haloes only depends on halo mass. However, it has been long established that the clustering of haloes also depends on other properties, including formation time, concentration, and spin; this effect is commonly known as halo assembly bias. We use a suite of gravity-only simulations to study the dependence of halo assembly bias on cosmology; these simulations cover cosmological parameters spanning 10$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.
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.
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.
Using samples drawn from the Sloan Digital Sky Survey, we study the relationship between local galaxy density and the properties of galaxies on the red sequence. After removing the mean dependence of average overdensity (or environment) on color and luminosity, we find that there remains a strong residual trend between luminosity-weighted mean stellar age and environment, such that galaxies with older stellar populations favor regions of higher overdensity relative to galaxies of like color and luminosity (and hence of like stellar mass). Even when excluding galaxies with recent star-formation activity (i.e., younger mean stellar ages) from the sample, we still find a highly significant correlation between stellar age and environment at fixed stellar mass. This residual age-density relation provides direct evidence for an assembly bias on the red sequence such that galaxies in higher-density regions formed earlier than galaxies of similar mass in lower-density environments. We discuss these results in the context of the age-metallicity degeneracy and in comparison to previous studies at low and intermediate redshift. Finally, we consider the potential role of assembly bias in explaining recent results regarding the evolution of post-starburst (or post-quenching) galaxies and the environmental dependence of the type Ia supernova rate.
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