Class
NumCosmoClusterMassAscaso
Description [src]
final class NumCosmo.ClusterMassAscaso : NumCosmo.ClusterMassRichness
{
/* No available fields */
}Cluster mass-richness distribution model based on Ascaso et al.
This class implements the Ascaso mass-richness relation, which models the mean and scatter of the log-richness distribution as linear functions of mass and redshift.
The mean log-richness is given by: $$ \mu = \mu_{p0} + \mu_{p1} \Delta\ln M + \mu_{p2} \Delta\ln(1+z) $$ where $\Delta\ln M = \ln M - \ln M_0$ and $\Delta\ln(1+z) = \ln(1+z) - \ln(1+z_0)$.
The standard deviation is given by: $$ \sigma = \sigma_{p0} + \sigma_{p1} \Delta\ln M + \sigma_{p2} \Delta\ln(1+z) $$.
Instance methods
Methods inherited from NcClusterMassRichness (12)
nc_cluster_mass_richness_compute_truncated_mean
Computes the mean of the truncated log-richness distribution given the untruncated mean and standard deviation. The distribution is truncated at the cut threshold.
nc_cluster_mass_richness_compute_truncated_std
Computes the standard deviation of the truncated log-richness distribution given the untruncated mean and standard deviation. The distribution is truncated at the cut threshold.
nc_cluster_mass_richness_get_cut
Gets the cut in richness.
nc_cluster_mass_richness_get_mean
Computes the mean of the truncated richness distribution.
nc_cluster_mass_richness_get_sample_full_dist
Gets the current sampling strategy.
nc_cluster_mass_richness_get_std
Computes the standard deviation of the truncated richness distribution.
nc_cluster_mass_richness_ln1pz0
Gets the natural logarithm of (1 + pivot redshift).
nc_cluster_mass_richness_lnM0
Gets the natural logarithm of the pivot mass.
nc_cluster_mass_richness_mu
Computes the mean of the log-richness distribution as a function of mass and redshift.
nc_cluster_mass_richness_mu_sigma
Computes both the mean and standard deviation of the log-richness distribution
simultaneously. This can be more efficient than calling mu() and sigma()
separately when subclasses share intermediate computations.
nc_cluster_mass_richness_set_sample_full_dist
Sets the sampling strategy for richness values.
nc_cluster_mass_richness_sigma
Computes the standard deviation of the log-richness distribution as a function of mass and redshift.
Methods inherited from NcClusterMass (15)
nc_cluster_mass_free
Atomically decrements the reference count of clusterm by one. If the reference count drops to 0,
all memory allocated by clusterm is released.
nc_cluster_mass_intp
It computes the clusterm probability distribution of lnM lying
in the range $[]$, namely,
$$ intp = \int_{\ln M^{obs}{min}}^{\ln M^{obs}{max}} p \, d\ln M^{obs},$$
where $p$ is [nc_cluster_mass_p()].
nc_cluster_mass_intp_bin
Computes the integrated probability over the observed mass bin.
nc_cluster_mass_n_limits
Computes the mass limits for the cluster abundance calculation.
The function which will call this one is responsible to allocate memory for lnM_lower and lnM_upper.
nc_cluster_mass_p
Computes the probability distribution $P(\ln M_{\mathrm{obs}}|\ln M, z)$.
nc_cluster_mass_p_bin_limits
Computes the integration limits for the true mass given the observed mass bin boundaries.
nc_cluster_mass_p_limits
Computes the integration limits for the true mass given the observed mass and its parameters.
nc_cluster_mass_p_vec_z_lnMobs
This method computes the probability distribution of lnM for each redshift in z
given the true mass lnM and the observed mass proxies lnM_obs and their parameters lnM_obs_params.
nc_cluster_mass_plcl_Msz_Ml_p_ndetone
This function computes the i-th term of the posterior given flat priors for the selection function and mass function. See function nc_cluster_pseudo_counts_posterior_ndetone().
nc_cluster_mass_plcl_pdf
Compute the joint probability density used internally by the PL-CL mass model. Integrals in $M_{sz}$ and $M_l$ are performed in the dimensionless quantities $\ln (M_{sz} / M_0)$ and $\ln (M_l / M_0)$, respectively. The Gaussian distributions between $M_{Pl}$ and $M_{CL}$ are written in terms of the dimensionless quantities $M_{Pl}/M_0$, $M_{CL}/M_0$, $\sigma_{Pl}/M_0$ and $\sigma_{CL}/M_0$.
nc_cluster_mass_plcl_pdf_only_lognormal
nc_cluster_mass_ref
Increases the reference count of clusterm by one.
nc_cluster_mass_resample
Generates a random sample of the observed mass proxies given the true mass and redshift.
nc_cluster_mass_resample_vec
Generates a random sample of the observed mass proxies given the true mass and redshift.
This is a convenience wrapper around nc_cluster_mass_resample() that uses NcmVector
for proper Python bindings support.
nc_cluster_mass_volume
Computes the effective volume in the observable mass space.
Properties
Properties inherited from NcClusterMassRichness (7)
NumCosmo.ClusterMassRichness:M0
Pivot mass in the richness-mass scaling relation.
NumCosmo.ClusterMassRichness:cut
NumCosmo.ClusterMassRichness:cut-fit
NumCosmo.ClusterMassRichness:lnRichness-max
Maximum logarithm (base e) of richness for cluster selection.
NumCosmo.ClusterMassRichness:lnRichness-min
Minimum logarithm (base e) of richness for cluster selection.
NumCosmo.ClusterMassRichness:sample-full-dist
Controls the sampling strategy for richness values:.
NumCosmo.ClusterMassRichness:z0
Pivot redshift in the richness-mass scaling relation.
Properties inherited from NcmModel (9)
NumCosmoMath.Model:implementation
NumCosmoMath.Model:name
NumCosmoMath.Model:nick
NumCosmoMath.Model:params-types
NumCosmoMath.Model:reparam
NumCosmoMath.Model:scalar-params-len
NumCosmoMath.Model:sparam-array
NumCosmoMath.Model:submodel-array
NumCosmoMath.Model:vector-params-len
Signals
Signals inherited from GObject (1)
GObject::notify
The notify signal is emitted on an object when one of its properties has its value set through g_object_set_property(), g_object_set(), et al.