Zu & Mandelbaum et al. (2016) Composite Model¶
This section of the documentation describes the basic behavior of
the zu_mandelbaum16 composite HOD model. To see how this composite
model is built by the PrebuiltHodModelFactory class,
see zu_mandelbaum16_model_dictionary.
Overview of the Zu & Mandelbaum et al. (2016) Model Features¶
This HOD-style model is based on Zu & Mandelbaum et al (2016). The behavior of this model is governed by the Behroozi et al. (2010), but with parameters that have been refit to z=0 data, and with scatter that is allowed to vary with halo mass. The occupation statistics have the same functional form as the Leauthaud et al. (2011) Composite Model introduced in Leauthaud et al (2011).
In this model, there are two populations, centrals and satellites.
Central occupation statistics are given by a nearest integer distribution
with first moment given by an erf function; the class governing this
behavior is ZuMandelbaum15Cens.
Central galaxies are assumed to reside at the exact center of the host halo;
the class governing this behavior is TrivialPhaseSpace.
Satellite occupation statistics are given by a Poisson distribution
with first moment given by a power law that has been truncated at the low-mass end;
the class governing this behavior is ZuMandelbaum15Sats;
satellites in this model follow an (unbiased) NFW profile, as governed by the
NFWPhaseSpace class.
Each model galaxy is assigned a binary designation for quiescent.
This modeling is done separately for centrals and satellites, with the
ZuMandelbaum16QuenchingCens and
ZuMandelbaum16QuenchingSats, respectively.
Building the Zu & Mandelbaum et al. (2016) Model¶
You can build an instance of this model using the
PrebuiltHodModelFactory class as follows:
>>> from halotools.empirical_models import PrebuiltHodModelFactory
>>> model = PrebuiltHodModelFactory('zu_mandelbaum16')
Customizing the Zu & Mandelbaum et al. (2016) Model¶
There are two keyword arguments you can use to customize the instance returned by the factory:
First, the threshold keyword argument pertains to the minimum
stellar mass of the galaxy sample, in solar mass units with h=1:
>>> model = PrebuiltHodModelFactory('zu_mandelbaum16', threshold=10.75)
Second, the prim_haloprop_key keyword argument determines which
halo mass definition will be used to populate a mock with this model.
You are free to choose any halo mass definition you like, but you should
be aware that the best-fit parameters of the Zu & Mandelbaum model are
based on halo_m200m:
>>> model = PrebuiltHodModelFactory('zu_mandelbaum16', threshold=11, haloprop_key='halo_mvir')
The Colossus python package written by Benedikt Diemer can be used to convert between different halo mass definitions. This may be useful if you wish to use an existing halo catalog for which the halo mass definition you need is unavailable.
As described in Changing Composite Model Parameters, you can always change the model parameters
after instantiation by changing the values in the param_dict dictionary. For example,
>>> model.param_dict['alphasat'] = 1.1
The above line of code changes the power law slope between halo mass and satellite occupation number, \(\langle N_{\rm sat} \rangle \propto M_{\rm halo}^{\alpha}\). See Parameters of the Zu & Mandelbaum et al. (2016) model for a description of all parameters of this model.
Populating Mocks and Generating Zu & Mandelbaum et al. (2016) Model Predictions¶
As with any Halotools composite model, the model instance
can populate N-body simulations with mock galaxy catalogs.
In the following, we’ll show how to do this
with fake simulation data via the halocat argument.
>>> from halotools.sim_manager import FakeSim
>>> halocat = FakeSim()
>>> model = PrebuiltHodModelFactory('zu_mandelbaum16')
>>> model.populate_mock(halocat)
See ModelFactory.populate_mock for information about how to
populate your model into different simulations.
See Tutorials on analyzing galaxy catalogs for a sequence of worked examples
on how to use the mock_observables sub-package
to study a wide range of astronomical statistics predicted by your model.
Studying the Zu & Mandelbaum et al. (2016) Model Features¶
In addition to populating mocks, the zu_mandelbaum16 model also gives you access to
its underlying analytical relations. Here are a few examples:
>>> import numpy as np
>>> halo_mass = np.logspace(11, 15, 100)
To compute the mean number of each galaxy type as a function of halo mass:
>>> mean_ncen = model.mean_occupation_centrals(prim_haloprop=halo_mass)
>>> mean_nsat = model.mean_occupation_satellites(prim_haloprop=halo_mass)
To compute the mean stellar mass of central galaxies as a function of halo mass:
>>> mean_sm_cens = model.mean_stellar_mass_centrals(prim_haloprop=halo_mass)
Now suppose you wish to know the mean halo mass of a central galaxy with known stellar mass:
>>> stellar_mass = np.logspace(9, 12, 100)
>>> inferred_halo_mass = model.mean_halo_mass_centrals(stellar_mass)
Parameters of the Zu & Mandelbaum et al. (2016) model¶
The best way to learn what the parameters of a model do is to just play with the code: change parameter values, make plots of how the underying analytical relations vary, and also of how the mock observables vary. Here we just give a simple description of the meaning of the parameters controlling the quiescent fractions of the model. For a description of the parameters controlling stellar mass, see the Zu & Mandelbaum et al. (2015) Composite Model tutorial.
You can also refer to the original publications Leauthaud et al (2011), Behroozi et al. (2010), and Zu & Mandelbaum (2015) and Zu & Mandelbaum (2016) for more detailed descriptions of the meaning of each parameter.
To see how the following parameters are implemented, see the source code of
ZuMandelbaum16QuenchingCens.mean_quiescent_fraction and
ZuMandelbaum16QuenchingSats.mean_quiescent_fraction. Mathematically,
param_dict[‘quenching_mass_gal_type’] - Characteristic halo mass where the quiescent fraction rapidly increases.
param_dict[‘quenching_exp_power_gal_type’] - Exponential power in the quiescent fraction function.