bornraytrace package

bornraytrace.intrinsic_alignments module

bornraytrace.intrinsic_alignments.D_1(z, om0)

Normalised linear growth factor (D_plus)

Parameters:

• z – single redshift value or array values

• om0 – matter density

Returns:

normalised linear growth factor

bornraytrace.intrinsic_alignments.D_single(z, om0)

Provides the normalised linear growth factor

Parameters:

• z – single redshift value

• om0 – matter density

Returns:

normalised linear growth factor

bornraytrace.intrinsic_alignments.E_sq(z, om0)

A function giving Hubble’s law for flat cosmology

Parameters:

• z – redshift value

• om0 – matter density

Returns:

A value for the Hubble parameter

bornraytrace.intrinsic_alignments.F_nla(z, om0, A_ia, rho_c1, eta=0.0, z0=0.0, lbar=0.0, l0=1e-09, beta=0.0)

NLA intrinsic alignment amplitude

Parameters:

• z – redshift value

• om0 – matter density

• A_ia – amplitude parameter

• rho_c1 – rho_crit x C1 (C1 approx 1.508e+27 cm3 / g)

• eta – redshift dependence

• z0 – arbitrary redshift pivot parameter

• lbar – average luminosity of source galaxy population

• l0 – arbitrary luminosity pivot parameter

• beta – luminosity dependence

Returns:

NLA F(z) amplitude

bornraytrace.intrinsic_alignments.f_integrand(z, om0)

A function for the redshift integrand in the intrinsic alignment calculation

Parameters:

• z – redshift value

• om0 – matter density

Returns:

redshift integrand

bornraytrace.lensing module

bornraytrace.lensing.W_kernel(r_array, z_array, nz, simpsons=False)

lensing kernel W s.t. kappa = prefactor * integral W(r) * overdensity(r) dr

Parameters:

• r_array – comoving distances array

• z_array – redshift array matching r_array (cosmology dependent)

• nz – source redshift distribution

• simpsons – boolean to use simpsons integratio

Returns:

W = r * q /r

bornraytrace.lensing.get_neighbour_array(nside)

array of indices labelling the 8 neighbouring pixels for each pixel

Parameters:

nside – nside of map

Returns:

neighbour indices array

bornraytrace.lensing.kappa2shear(kappa_map, lmax, downsample_nside=None)

Performs inverse Kaiser-Squires on the sphere with healpy spherical harmonics

Parameters:

• kappa_map – healpix format complex convergence (kappa) map

• lmax – maximum multipole

• downsample – option to downsample map output. A good compromise choice to reduce nside by half is: lmax=nside*2, downsample_nside=nside/2

Returns:

complex shear map (gamma1 + 1j * gamma2)

bornraytrace.lensing.kappa_prefactor(H0, om0, length_unit='Mpc')

Gives prefactor (3 H_0^2 Om0)/2

Parameters:

• H0 – Hubble parameter with astropy units

• om0 – Omega matter

• length_unit – for H0 (default Mpc)

Returns:

prefactor for lensing

bornraytrace.lensing.peak_find(map_input, nside, neighbour_array=None)

Find peaks (local maxima) for a given input map

Parameters:

• map_input – input map

• nside – nside of map

• neighbour_array – optional array of indices labelling the 8 neighbouring pixels for each pixel

Returns:

list of pixel indices for the peaks

bornraytrace.lensing.raytrace(H0, om0, overdensity_array, a_centre, comoving_edges, mask=None, Hubble_length_unit='Mpc')

Evaluate weak lensing convergence map using Born approximation

Parameters:

• H0 – Hubble parameter with astropy units

• om0 – Omega matter

• overdensity_array – an 2D array of overdensity healpix maps in radial shells

• a_centre – scale factor at comoving centre of shells

• comoving_edges – comoving distance to edges of shells

• mask – healpix map where 1 is observed and 0 is mask

• length_unit – for H0 (default Mpc)

Returns:

convergence kappa map

bornraytrace.lensing.raytrace_integration(kappa_prefactor, overdensity_array, a_centre, comoving_edges, mask=None)

This function evaluates the Born weak lensing integral

Parameters:

• kappa_prefactor – defined as the output of the function kappa_prefactor

• overdensity_array – an 2D array of overdensity healpix maps in radial shells

• a_centre – scale factor at comoving centre of shells

• comoving_edges – comoving distance to edges of shells

• mask – healpix map where 1 is observed and 0 is mask

Returns:

convergence kappa map

bornraytrace.lensing.recentre_nz(z_sim_edges, z_samp_centre, nz_input)

Takes input n(z) sampled at z_samp_centre and evaluates interpolated n(z) at new z values to match a simulation at z_sim_edges

Parameters:

• z_sim_edges – new z values for n(z)

• z_samp_centre – original z values for n(z)

• nz_input – original n(z)

Returns:

new n(z)

bornraytrace.lensing.rotate_mask_approx(mask, rot_angles, flip=False)

rotate healpix mask on sphere

Parameters:

• mask – healpix map of ones and zeros

• rot_angles – rotation on the sphere (e.g. [ 45.91405291 ,150.72092269 , 46.34505909])

• flip – boolean, mirror the mask

Returns:

rotated map

bornraytrace.lensing.shear2kappa(shear_map, lmax, upsample=False)

Performs Kaiser-Squires on the sphere with healpy spherical harmonics

Parameters:

• shear_map – healpix format complex shear map

• lmax – maximum ell multipole

• upsample – option to upsample map for transforms to mitigate numerical errors

Returns:

kappa map