ComputeCls

class cosmicjellyfish.LSSsurvey.photo_obs.ComputeCls(cosmopars, photopars, IApars, biaspars, print_info_specs=False, fiducial_cosmo=None, *, configuration=None)

Bases: object

compute_all()

Main function to compute the angular power spectrum. Will first compute the limber approximated angular power spectrum and the window functions for both probes. From that it will obtain the angular power spectrum.

Note

This function does not return the calculated quantities. Rather they are found in new attributes of the object.

The result of the Limber approximated power spectra are found in the new attributes Pell and sqrtPell

The window functions are found in the new attributes GC if galaxy clustering is asked for, and WL if cosmic sheer is asked for

The angular power spectrum is found in the new attribute result

print_numerical_specs()

prints the numerical specifications of the internal computations

P_limber()

Calculates the Limber-approximated power spectrum. This is done for a range of redshift values and multipoles. Will add MG effects to WL and(!) GCph if asked for.

Note

This does not return the power spectra. The results are stored in the attribute Pell

sqrtP_limber()

Calculates the square root of the Limber-approximated power spectrum for weak lensing (WL) and galaxy clustering photometric (GCph) observables. This is done for a range of redshift values and multipoles. Depending on the tracer asked for in ‘GCph_Tracer’ will use ‘matter’ or ‘clustering’ observable GCph. Will add MG effects to WL if asked for.

Note

This does not return the power spectra. The results are stored in the attribute sqrtPell

galaxy_kernel(z, i)

Calculates the GCph kernel function

Parameters:

• z (numpy.ndarray) – array containing the redshifts for which the kernel should be computed for

• i (int) – index of the redshift bin

Returns:

Value of the galaxy clustering kernel at redshift z for bin i

Return type:

numpy.ndarray

Note

Implements the following equation:

\[W_i^{GCph} = b(z) \frac{n_i(z)}{\bar{n}(z)} H(z)\]

lensing_kernel(z, i)

Computes the photometric cosmic shear kernel function

Parameters:

• z (numpy.ndarray) – array containing the redshifts for which the kernel should be computed for

• i (int) – index of the redshift bin

Returns:

Value of the cosmic shearing kernel at redshift z for bin i

Return type:

numpy.ndarray

Note

Implements the following equation:

\[W_i^{WL} = W_i^{IA}+\frac{3}{2}\left(\frac{H_0}{c}\right)^2\Omega_{m,0}(1+z)r(z) \int_z^{z_{\rm max}}{dz' \frac{n_i(z')}{\bar{n}(z)}\left[1-\frac{r(z)}{r(z')}\right]}\]

integral_efficiency(i)

computes the integral that enters the lensing kernel for a given redshift bin

Parameters:

i (int) – index of the redshift bin for which the lensing efficiency should be calculated

Returns:

callable function that receives a numpy.ndarray of requested redshifts and returns the lensing efficiency for the i-th bin as a numpy.ndarray

Return type:

callable

lensing_efficiency()

computes the integral that enters the lensing kernel for all redshift bins

Returns:

list of callable functions that give the lensing efficiency for each bin

Return type:

list

compute_kernels()

function to compute all the needed window functions.

Note

This does not return the window functions. They are found in the new attributes GC if galaxy clustering is asked for, and WL if cosmic sheer is asked for

genwindow(z, obs, i)

generic function to obtain the window functions for the different observables.

Parameters:

• z (numpy.ndarray) – array of redshifts for which the window function should be computed

• obs (str) – name of the observable (GC or WL)

• i (int) – integer of the redshift bin the window should be computed for

Returns:

Values of the window function for the observable obs, for redshift bin i, and at redshifts z.

Return type:

numpy.ndarray

computecls()

Function to compute the angular power spectrum for all observables, redshift bins and multipoles.

Returns:

a dictionary containing all auto and cross correlation angular power spectra. Its keys are formatted to have an array of the power spectra in ‘X ixY j’. Also the multipoles for which the angular power spectra were computed for are found in ‘ells’.

Return type:

dict

Note

Implements the following equation:

\[C_{i,j}^{X,Y}(\ell) = c \int \mathrm{d}z \frac{W_i^X (z)W_j^Y (z)}{ H(z) r^2(z)} P_{\delta \delta} \big[\frac{\ell + 1/2}{r(z)} , z \big]\]

computecls_vectorized()

Vectorized function to compute the angular power spectrum for all observables, redshift bins and multipoles.

Returns:

a dictionary containing all auto and cross correlation angular power spectra. Its keys are formatted to have an array of the power spectra in ‘X ixY j’. Also the multipoles for which the angular power spectra were computed for are found in ‘ells’.

Return type:

dict

Note

Implements the following equation:

\[C_{i,j}^{X,Y}(\ell) = c \int \mathrm{d}z \frac{W_i^X (z)W_j^Y (z)}{ H(z) r^2(z)} P_{\delta \delta} \big[\frac{\ell + 1/2}{r(z)} , z \big]\]

clsintegral(obs1, obs2, bin1, bin2, hub)

function to obtain the angular power spectrum as an function of the multipole.

Parameters:

• obs1 (str) – name of the observable of the redshift bin bin1 (GC or WL)

• obs2 (str) – name of the observable of the redshift bin bin2 (GC or WL)

• bin1 (int) – index of the redshift bin at which the observable obs1 is measured

• bin2 (int) – index of the redshift bin at which the observable obs2 is measured

• hub (callable) – function that should be passed a numpy.ndarray of redshifts returns the hubble expansion rate at these redshifts.

Returns:

Function that receives an array multipole and returns the angular power spectrum for these multipoles.

Return type:

callable