crow.cluster_modules.completeness_models module

The cluster completeness module.

This module holds the classes that define completeness kernels that can be included in the cluster prediction integrand.

class crow.cluster_modules.completeness_models.Completeness

Bases: object

The completeness kernel base class.

This kernel affects the prediction integrand by accounting for the incompleteness of a cluster selection. Subclasses should implement the distribution method.

Variables:

parameters (Parameters, optional) -- Container for completeness model parameters (defined by subclasses).

distribution(log_mass: ndarray[tuple[Any, ...], dtype[float64]], z: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

Evaluate the completeness kernel contribution.

Parameters:

• log_mass (array_like) -- Array of log10 halo masses (units: Msun).

• z (array_like) -- Array of redshifts matching log_mass.

Returns:

Array of completeness values in the range [0, 1] with the same broadcastable shape as the inputs. Subclasses should guarantee the output dtype is floating point.

Return type:

numpy.ndarray

class crow.cluster_modules.completeness_models.CompletenessAguena16

Bases: Completeness

Completeness model following Aguena et al. (2016) parametrisation.

The model uses a pivot mass and a redshift-dependent power-law index to compute a sigmoid-like completeness as a function of mass and redshift.

Parameters:

• initialization) ((set during)

• a_n (float) -- Parameters controlling the redshift evolution of the power-law index.

• b_n (float) -- Parameters controlling the redshift evolution of the power-law index.

• a_logm_piv (float) -- Parameters controlling the pivot mass (in log10 units) and its redshift evolution.

• b_logm_piv (float) -- Parameters controlling the pivot mass (in log10 units) and its redshift evolution.

Variables:

parameters (Parameters) -- Container holding the parameter values; defaults are defined in REDMAPPER_DEFAULT_PARAMETERS.

distribution(log_mass: ndarray[tuple[Any, ...], dtype[float64]], z: ndarray[tuple[Any, ...], dtype[float64]]) → ndarray[tuple[Any, ...], dtype[float64]]

Compute the completeness fraction for given mass and redshift. The completeness is given by

\[c(M, z) = \frac{\left(M / M_{\rm piv}(z)\right)^{n_c(z)}} {1 + \left(M / M_{\rm piv}(z)\right)^{n_c(z)}}\]

where M = 10^{text{log_mass}}, M_{rm piv}(z) is returned by _mpiv(z), and n_c(z) is returned by _nc(z).

Parameters:

• log_mass (array_like) -- Array of log10 halo masses (Msun).

• z (array_like) -- Array of redshifts matching log_mass.

Returns:

Completeness values in the interval [0, 1] with shape matching the broadcasted inputs. dtype is float64.

Return type:

numpy.ndarray