firecrown.generators.inferred_galaxy_zdist.ZDistLSSTSRD

firecrown.generators.inferred_galaxy_zdist.ZDistLSSTSRD#

class firecrown.generators.inferred_galaxy_zdist.ZDistLSSTSRD(alpha, beta, z0, max_z=5.0, use_autoknot=False, autoknots_reltol=0.0001, autoknots_abstol=1e-15)[source]#

Bases: object

LSST Inferred galaxy redshift distributions.

Inferred galaxy redshift distribution is based on the LSST Science Requirements Document (SRD), equation 5. Note that the SRD fixes $beta = 2$.

The values of $alpha$ and $z_0$ are different for Year 1 and Year 10. ZDistLLSTSRD provides these values as defaults and allows for greater flexibility when desired.

Parameters:

  • alpha (float) –

  • beta (float) –

  • z0 (float) –

  • max_z (float) –

  • use_autoknot (bool) –

  • autoknots_reltol (float) –

  • autoknots_abstol (float) –

Public Methods:

__init__(alpha, beta, z0[, max_z, ...])

Initialize the LSST Inferred galaxy redshift distribution.

year_1_lens([alpha, beta, z0])

Create a ZDistLSSTSRD object for the first year of LSST.

year_1_source([alpha, beta, z0])

Create a ZDistLSSTSRD object for the first year of LSST.

year_10_lens([alpha, beta, z0])

Create a ZDistLSSTSRD object for the tenth year of LSST.

year_10_source([alpha, beta, z0])

Create a ZDistLSSTSRD object for the tenth year of LSST.

distribution(z)

Generate the inferred galaxy redshift distribution.

distribution_zp(zp, sigma_z)

Generate the Gaussian convolution of the distribution.

compute_distribution(sigma_z)

Generate the inferred galaxy redshift distribution.

compute_true_distribution()

Generate the inferred galaxy redshift distribution.

equal_area_bins(n_bins, sigma_z, last_z[, ...])

Generate equal area bins for the distribution.

binned_distribution(*, zpl, zpu, sigma_z, z, ...)

Generate the inferred galaxy redshift distribution in bins.

Private Methods:

_integrated_gaussian_scalar(zpl, zpu, sigma_z, z)

Generate the integrated Gaussian distribution.

_integrated_gaussian(zpl, zpu, sigma_z, z)

Generate the integrated Gaussian distribution.
_integrated_gaussian(zpl, zpu, sigma_z, z)[source]#

Generate the integrated Gaussian distribution.

Parameters:

  • zpl (float) –

  • zpu (float) –

  • sigma_z (float) –

  • z (ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]) –

Return type:

ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

_integrated_gaussian_scalar(zpl, zpu, sigma_z, z)[source]#

Generate the integrated Gaussian distribution.

Parameters:

  • zpl (float) – The lower bound of the integration

  • zpu (float) – The upper bound of the integration

  • sigma_z (float) – The resolution parameter

  • z (float) – The redshifts at which to evaluate the distribution

Return type:

float

Returns:

The integrated distribution

binned_distribution(*, zpl, zpu, sigma_z, z, name, measurements)[source]#

Generate the inferred galaxy redshift distribution in bins.

Parameters:

  • zpl (float) – The lower bound of the integration

  • zpu (float) – The upper bound of the integration

  • sigma_z (float) – The resolution parameter

  • z (ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]) – The redshifts at which to evaluate the distribution

  • name (str) – The name of the distribution

  • measurements (set[Galaxies | CMB | Clusters]) – The set of measurements of the distribution

Return type:

InferredGalaxyZDist

Returns:

The inferred galaxy redshift distribution

compute_distribution(sigma_z)[source]#

Generate the inferred galaxy redshift distribution.

Computes the distribution by convolving the true distribution with a Gaussian. The convolution is computed using the AutoKnots algorithm of NumCosmo.

Parameters:

sigma_z (float) – The resolution parameter

Return type:

StatsDist1d

Returns:

The inferred galaxy redshift distribution

compute_true_distribution()[source]#

Generate the inferred galaxy redshift distribution.

Computes the distribution without the convolution with the Gaussian. That is, the true redshift distribution.

Return type:

StatsDist1d

Returns:

The inferred galaxy redshift distribution

distribution(z)[source]#

Generate the inferred galaxy redshift distribution.

Parameters:

z (ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]) – The redshifts at which to evaluate the distribution

Return type:

ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

The inferred galaxy redshift distribution

distribution_zp(zp, sigma_z)[source]#

Generate the Gaussian convolution of the distribution.

Parameters:

  • sigma_z (float) – The resolution parameter

  • zp (float) – The photometric redshift

Return type:

float

Returns:

The Gaussian distribution

equal_area_bins(n_bins, sigma_z, last_z, use_true_distribution=False)[source]#

Generate equal area bins for the distribution.

In order to compute the bin edges, the convolution of the distribution with a Gaussian is computed. The bin edges are then computed by inverting the cumulative distribution function of the convolution.

If the true distribution is used, the convolution is not computed. This provides a faster way to compute the bin edges.

Parameters:

  • n_bins (int) – The number of bins

  • sigma_z (float) – The resolution parameter

  • last_z (float) – The last redshift to consider

  • use_true_distribution (bool) – Whether to use the true distribution

Return type:

ndarray[tuple[int, ...], dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

The bin edges

classmethod year_10_lens(alpha=0.9, beta=2.0, z0=0.28, **kwargs)[source]#

Create a ZDistLSSTSRD object for the tenth year of LSST.

It uses the default values of the alpha, beta and z0 parameters from the LSST SRD Year 10 for the lens distribution.

Parameters:

  • alpha (float) – The alpha parameter of the distribution

  • beta (float) – The beta parameter of the distribution

  • z0 (float) – The z0 parameter of the distribution

  • kwargs (Unpack[ZDistLSSTSRDOpt]) –

Return type:

ZDistLSSTSRD

Returns:

A ZDistLSSTSRD object.

classmethod year_10_source(alpha=0.68, beta=2.0, z0=0.11, **kwargs)[source]#

Create a ZDistLSSTSRD object for the tenth year of LSST.

It uses the default values of the alpha, beta and z0 parameters from the LSST SRD Year 10 for the source distribution.

Parameters:

  • alpha (float) – The alpha parameter of the distribution

  • beta (float) – The beta parameter of the distribution

  • z0 (float) – The z0 parameter of the distribution

  • kwargs (Unpack[ZDistLSSTSRDOpt]) –

Return type:

ZDistLSSTSRD

Returns:

A ZDistLSSTSRD object.

classmethod year_1_lens(alpha=0.94, beta=2.0, z0=0.26, **kwargs)[source]#

Create a ZDistLSSTSRD object for the first year of LSST.

It uses the default values of the alpha, beta and z0 parameters from the LSST SRD Year 1 for the lens distribution.

Parameters:

  • alpha (float) – The alpha parameter of the distribution

  • beta (float) – The beta parameter of the distribution

  • z0 (float) – The z0 parameter of the distribution

  • kwargs (Unpack[ZDistLSSTSRDOpt]) –

Return type:

ZDistLSSTSRD

Returns:

A ZDistLSSTSRD object.

classmethod year_1_source(alpha=0.78, beta=2.0, z0=0.13, **kwargs)[source]#

Create a ZDistLSSTSRD object for the first year of LSST.

It uses the default values of the alpha, beta and z0 parameters from the LSST SRD Year 1 for the source distribution.

Parameters:

  • alpha (float) – The alpha parameter of the distribution

  • beta (float) – The beta parameter of the distribution

  • z0 (float) – The z0 parameter of the distribution

  • kwargs (Unpack[ZDistLSSTSRDOpt]) –

Return type:

ZDistLSSTSRD

Returns:

A ZDistLSSTSRD object.