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Zheng07_HOD_Model

class halotools.halo_occupation.Zheng07_HOD_Model(parameter_dict=None, threshold=None)[source] [edit on github]

Bases: halotools.halo_occupation.HOD_Model

Subclass of HOD_Model object, where functional forms for occupation statistics are taken from Zheng et al. 2007, arXiv:0703457.

Parameters:

parameter_dict : dictionary, optional.

Contains values for the parameters specifying the model. Dictionary keys are ‘logMmin_cen’, ‘sigma_logM’, ‘logM0_sat’,’logM1_sat’,’alpha_sat’. Default values pertain to the best-fit values of their M_{r} - 5log_{10}h< -19.5 threshold sample.

All the best-fit parameter values provided in Table 1 of Zheng et al. (2007) can be accessed via the published_parameters method.

threshold : float, optional.

Luminosity threshold of the mock galaxy sample. If specified, input value must agree with one of the thresholds used in Zheng07 to fit HODs: [-18, -18.5, -19, -19.5, -20, -20.5, -21, -21.5, -22]. Default value is -20, specified in the defaults module.

Notes

c-M_{vir} relation is current set to be Anatoly’s, though this is not the relation used in Zheng07. Their concentration-mass relation is of the same form as the one implemented one, but with different values for the hard-coded parameters. See Equation 1 of arXiv:0703457.

Attributes Summary

primary_halo_property_key Model is based on M = M_{vir}.

Methods Summary

mean_concentration(logM, halo_type) Concentration-mass relation of the model.
mean_ncen(logM, halo_type) Expected number of central galaxies in a halo of mass logM.
mean_nsat(logM, halo_type) Expected number of satellite galaxies in a halo of mass logM.
published_parameters() Best-fit HOD parameters from Table 1 of Zheng et al.
require_correct_keys() If a parameter dictionary is passed to the class upon instantiation, this method is used to enforce that the set of keys is in accord with the set of keys required by the model.

Attributes Documentation

primary_halo_property_key

Model is based on M = M_{vir}.

Methods Documentation

mean_concentration(logM, halo_type)[source] [edit on github]

Concentration-mass relation of the model.

Parameters:

logM : array_like

array of log_{10}(M) of halos in catalog

halo_type : array

array of halo types. Entirely ignored in this model. Included as a passed variable purely for consistency between the way this function is called by different models.
Returns:

concentrations : array_like

Mean concentration of halos of the input mass, using anatoly_concentration model.
mean_ncen(logM, halo_type)[source] [edit on github]

Expected number of central galaxies in a halo of mass logM. See Equation 2 of arXiv:0703457.

Parameters:

logM : array

array of log_{10}(M) of halos in catalog

halo_type : array

array of halo types. Entirely ignored in this model. Included as a passed variable purely for consistency between the way this function is called by different models.
Returns:

mean_ncen : array

Notes

Mean number of central galaxies in a host halo of the specified mass.

\langle N_{cen} \rangle_{M} = \frac{1}{2}\left( 1 + erf\left( \frac{log_{10}M - log_{10}M_{min}}{\sigma_{log_{10}M}} \right) \right)

mean_nsat(logM, halo_type)[source] [edit on github]

Expected number of satellite galaxies in a halo of mass logM. See Equation 5 of arXiv:0703457.

Parameters:

logM : array

array of log_{10}(M) of halos in catalog

halo_type : array

array of halo types. Entirely ignored in this model. Included as a passed variable purely for consistency between the way this function is called by different models.
Returns:

mean_nsat : float or array

Mean number of satellite galaxies in a host halo of the specified mass.

\langle N_{sat} \rangle_{M} = \left( \frac{M - M_{0}}{M_{1}} \right)^{\alpha} \langle N_{cen} \rangle_{M}
published_parameters()[source] [edit on github]

Best-fit HOD parameters from Table 1 of Zheng et al. 2007.

Parameters:

threshold : float

Luminosity threshold defining the SDSS sample to which Zheng et al. fit their HOD model. Must be agree with one of the published values: [-18, -18.5, -19, -19.5, -20, -20.5, -21, -21.5, -22].
Returns:

parameter_dict : dict

Dictionary of model parameters whose values have been set to agree with the values taken from Table 1 of Zheng et al. 2007.
require_correct_keys()[source] [edit on github]

If a parameter dictionary is passed to the class upon instantiation, this method is used to enforce that the set of keys is in accord with the set of keys required by the model.

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