Code for IA modelling. The modules here are designed to be plugged into a CosmoSIS pipeline.

Quite a bit of this stuff borrows and adapts from other people's codes. If you're interested in using any of it for published work, it's probably best if you get in touch (ssamurof@andrew.cmu.edu); I'd be happy to provide the appropriate references.

add_1h_ia:

• Summary: Reads pre-computed one and two halo power spectra from the block, and add them together.
• Language: python
• Inputs: 1h and 2h IA power spectra P^1h_GI(k,z), P^2h_GI(k,z), P^1h_II(k,z), P^2h_II(k,z)
• Outputs: Combined 1h+2h IA power spectrum P_GI(k,z), P_II(k,z)

flatten_pk:

• Summary: Reads a pre-computed power spectrum and integrates along the redshift direction. Also applies galaxy bias and IA amplitudes if desired.
• Language: python
• Inputs: IA power spectra P_GI(k,z),P_II(k,z); redshift distributions p_1(z),p_2(z)
• Outputs: Flattened spectra P_GI(k),P_II(k)

promote_ia_term:

• Summary: Reads a power spectrum, renames it.
• Language: python
• Inputs: Generic power spectrum P(k,z)
• Outputs: The same power spectrum, but now called something else in the data block P(k,z)

add_1h_ia:

• Summary: Computes a likelihood for some combination of wgg, wg+ and w++ 2pt data. Scale cuts and ordering specified in the params file.
• Language: python
• Inputs: Theory correlations wgg, wg+, w++; data vector wgg', wg+', w++'; covariance matrix C.
• Outputs: A likelihood and a chi2.

schneider_bridle:

• Summary: Computes IA power spectra using the fitting fuctions of https://arxiv.org/abs/0903.3870.
• Language: python
• Inputs: None
• Outputs: One halo intrinsic alignment power spectra P^1h_GI(k,z), P^1h_II(k,z)

projected_corrs_limber:

• Summary: Reads a flattened power spectrum and performs a Hankel transform with the appropriate Bessel function to generate wg+, wgg, w++ etc.
• Language: C
• Inputs: Flattened power spectra P(k)
• Outputs: Real space projected correlations as a function of perpendicular separation wg+(r_p), w++(r_p), wgg(r_p).

projected_corrs_legendre:

• Summary: Reads an IA power spectrum computes the line-of-sight projected correlations using Legendre polynomials.
• Language: python
• Inputs: IA power spectra P_GI(k,z), P_II(k,z).
• Outputs: Real space projected correlations as a function of perpendicular separation wg+(r_p), w++(r_p), wgg(r_p).

photometric_ias:

• Summary: Computes projected IA correlations in the case where one or both of the samples have finite redshift uncertainty (i.e. for photometric samples) using the prescription of https://arxiv.org/abs/1008.3491. Note that this process involves (a) computing C_ells via a series of Limber integrals, (b) a Hankel transform, and then (c) projecting in Pi and redshift. All of these steps are done internally (might take a few seconds, depending on settings), making use of CCL for (b).
• Language: python
• Inputs: IA power spectra P_GI(k,z), P_II(k,z).
• Outputs: real space projected correlations as a function of perpendicular separation wg+(r_p), w++(r_p), wgg(r_p).