Halo Density Profiles¶
This document describes the Colossus mechanisms for dealing with halo density profiles.
The halo density profile module is based on a powerful base class,
HaloDensityProfile. Some of the major design decisions are as follows:
The halo density profile is represented in physical units.
A halo density profile is split into two parts, an inner profile (1-halo term) and an outer profile (infalling matter plus 2-halo term). The outer profile consists of the sum of a number of possible terms, such as the mean density, a power law, and a 2-halo term based on the matter-matter correlation function. These terms can be added to any implementation of the inner profile.
There are two fundamental aspects to a model of the inner profile: it’s functional form, and the values of the parameters of this form. These parameters should be independent from each other, i.e., parameters should not be derivable from the other parameters.
Other quantities, such as settings and values derived from the parameters, are stored as so-called “options”. Options cannot be varied in a fit, and are not generally meant to be modified by the user once the profile has been instantiated.
The functional form cannot be changed once the profile object has been instantiated, i.e., the user cannot change the outer profile terms, density function etc.
The values of the parameters can be changed either directly by the user or during fitting. After such changes, the update() function must be called. Otherwise, internal variables may fall out of sync with the profile parameters.
Some profile forms may require knowledge of cosmological parameters and/or redshift, while some others do not (for example, an NFW profile without outer terms is a physical model that is independent of cosmology and redshift, whereas an outer term based on the mean density obviously relies on cosmological information). If a profile object relies on cosmology, the user needs to set a cosmology or an exception will be raised.
Almost all profile related functions are encapsulated within profile objects. For example, let us create an NFW profile for a halo with a particular virial mass and concentration:
from colossus.halo import profile_nfw profile = profile_nfw.NFWProfile(M = 1E12, mdef = 'vir', z = 0.0, c = 10.0) R200m = profile.RDelta(0.0, '200m') rho = profile.density(R200m) Sigma = profile.surfaceDensity(R200m) M200m = profile.enclosedMass(R200m)
Please consult the documentation of the abstract base class
HaloDensityProfile for the basic functionality of profile objects,
and The outer profile for instructions on how to add outer profile terms. For documentation
on spherical overdensity mass definitions, please see the documentation of the Halo Mass Definitions
module. For more examples of how to use the Colossus profile modules, see Tutorials.
Density profile models¶
The following functional forms for the density profile are currently implemented:
An arbitrary density profile
Diemer & Kravtsov profile
Creating a new profile class¶
It is easy to create a new form of the density profile in colossus. For example, let us create a Hernquist profile. This profile already exists in Colossus, but it is a suitable example nevertheless. All we have to do is:
Set the dictionaries for parameters and options to make our profile class “self-aware”
Call the super class’ constructor
Set the profile parameters that were passed to the constructor
Overwrite the density function (note that the function should be able to take either a number or a numpy array for the radius)
Here is the code:
from colossus.halo import profile_base class HernquistProfile(profile_base.HaloDensityProfile): def __init__(self, rhos, rs): self.par_names = ['rhos', 'rs'] self.opt_names =  profile_base.HaloDensityProfile.__init__(self) self.par['rhos'] = rhos self.par['rs'] = rs return def densityInner(self, r): x = r / self.par['rs'] density = self.par['rhos'] / x / (1.0 + x)**3 return density
This derived class inherits all the functionality of the parent class, including other physical
quantities (enclosed mass, surface density etc), derivatives, fitting to data, and the ability to
add outer profile terms. In order to make this class more convenient to use and faster, we could
improve it by letting the user pass mass and concentration to the constructor and computing
rs, and overwriting more methods such as the density derivative and enclosed
mass of the Hernquist profile.
Here, fitting refers to finding the parameters of a halo density profile which best describe a
given set of data points. Each point corresponds to a radius and a particular quantity, such as
density, enclosed mass, or surface density. Optionally, the user can pass uncertainties on the
data points, or even a full covariance matrix. All fitting should be done using the very general
fit() routine. For example, let us fit an NFW profile
to some density data:
profile = NFWProfile(M = 1E12, mdef = 'vir', z = 0.0, c = 10.0) profile.fit(r, rho, 'rho')
rho are arrays of radii and densities. Note that the current parameters of the
profile instance are used as an initial guess for the fit, and the profile object is set to the best-fit
parameters after the fit. Under the hood, the fit function handles multiple different fitting
methods. By default, the above fit is performed using a least-squares minimization, but we can also
use an MCMC sampler, for example to fit the surface density profile:
dict = profile.fit(r, Sigma, 'Sigma', method = 'mcmc', q_cov = covariance_matrix) best_fit_params = dict['x_mean'] uncertainty = dict['percentiles']
The following documents describe the general functionality of inner and outer profiles:
The following documents describe the specific implementations for each profile model: