crow.cluster_modules.mass_proxy.murata module

The Murata et al. 19 mass richness kernel models.

class crow.cluster_modules.mass_proxy.murata.MurataBinned(pivot_log_mass: float, pivot_redshift: float)

Bases: MurataModel, MassRichnessGaussian

Murata mass–richness relation for binned data vectors.

This implementation returns the integrated Gaussian probability over specified bins in the mass proxy.

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

Evaluate the binned mass–richness distribution.

Parameters:

• log_mass (ndarray of float64) -- Logarithm (base 10) of halo mass.

• z (ndarray of float64) -- Redshift values.

• log_mass_proxy_limits (tuple of float) -- Lower and upper bounds of the bin in log10 space.

Returns:

Integrated probability within the bin.

Return type:

ndarray of float64

class crow.cluster_modules.mass_proxy.murata.MurataModel(pivot_log_mass: float, pivot_redshift: float)

Bases: object

Base implementation of the Murata et al. (2019) mass–richness relation.

This model parameterizes the mean and scatter of the logarithmic mass proxy as linear functions of log-mass and redshift, defined relative to pivot values.

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

Compute the mean of the log mass proxy distribution.

Parameters:

• log_mass (ndarray of float64) -- Logarithm (base 10) of halo mass.

• z (ndarray of float64) -- Redshift values.

Returns:

Mean of the natural logarithm of the mass proxy.

Return type:

ndarray of float64

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

Compute the scatter of the log mass proxy distribution.

Parameters:

• log_mass (ndarray of float64) -- Logarithm (base 10) of halo mass.

• z (ndarray of float64) -- Redshift values.

Returns:

Standard deviation of the natural logarithm of the mass proxy.

Return type:

ndarray of float64

static observed_value(p: tuple[float, float, float], log_mass: ndarray[tuple[Any, ...], dtype[float64]], z: ndarray[tuple[Any, ...], dtype[float64]], pivot_ln_mass: float, log1p_pivot_redshift: float) → ndarray[tuple[Any, ...], dtype[float64]]

Compute a linear observable as a function of mass and redshift.

This function evaluates a linear model in deviations from pivot values.

Parameters:

• p (tuple of float) -- Model coefficients (p0, p1, p2).

• log_mass (ndarray of float64) -- Logarithm (base 10) of halo mass.

• z (ndarray of float64) -- Redshift values.

• pivot_ln_mass (float) -- Pivot value of natural log of mass.

• log1p_pivot_redshift (float) -- Pivot value of log(1 + z).

Returns:

Evaluated observable.

Return type:

ndarray of float64

class crow.cluster_modules.mass_proxy.murata.MurataUnbinned(pivot_log_mass: float, pivot_redshift: float)

Bases: MurataModel, MassRichnessGaussian

Murata mass–richness relation for unbinned data vectors.

This implementation evaluates the Gaussian probability density directly at the observed mass proxy values.

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

Evaluate the unbinned mass–richness distribution.

Parameters:

• log_mass (ndarray of float64) -- Logarithm (base 10) of halo mass.

• z (ndarray of float64) -- Redshift values.

• log_mass_proxy (ndarray of float64) -- Logarithm (base 10) of the observed mass proxy.

Returns:

Value of the Gaussian probability density function.

Return type:

ndarray of float64