plima.models.tatt#

TATT intrinsic alignment amplitude models.

This module provides array safe helpers for tidal alignment and tidal torquing amplitudes.

The functions here do not compute IA power spectra. They only prepare the redshift dependent amplitudes and normalized coefficients needed by downstream codes such as CCL or a Fisher forecasting pipeline.

Functions

tatt_amplitudes(z, *[, a1, a2, a1delta, ...])

Return redshift dependent TATT amplitudes.

tatt_normalized_coefficients(z, *, ...[, ...])

Return normalized TATT coefficients.

tatt_pt_biases(z, *, growth_factor, omega_m)

Return PT bias inputs for a TATT IA tracer.

unity_ia_bias(z)

Return a unity IA bias tuple.
plima.models.tatt.tatt_amplitudes(z, *, a1=1.0, a2=0.0, a1delta=0.0, eta1=0.0, eta2=0.0, eta1delta=0.0, z_pivot=0.62)[source]#

Return redshift dependent TATT amplitudes.

Parameters:

  • z (ArrayLike) – Redshift values.

  • a1 (float) – Linear tidal alignment amplitude at the pivot redshift.

  • a2 (float) – Quadratic tidal torquing amplitude at the pivot redshift.

  • a1delta (float) – Source density weighting amplitude at the pivot redshift.

  • eta1 (float) – Redshift evolution index for a1.

  • eta2 (float) – Redshift evolution index for a2.

  • eta1delta (float) – Redshift evolution index for a1delta.

  • z_pivot (float) – Pivot redshift.

Returns:

Dictionary containing a1, a2, and a1delta arrays.

Raises:

  • ValueError – If any numerical input is not finite.

  • ValueError – If any redshift value is less than or equal to -1.

  • ValueError – If z_pivot is less than or equal to -1.

Return type:

dict[str, ndarray[tuple[Any, …], dtype[float64]]]

plima.models.tatt.tatt_normalized_coefficients(z, *, growth_factor, omega_m, a1=1.0, a2=0.0, a1delta=0.0, eta1=0.0, eta2=0.0, eta1delta=0.0, z_pivot=0.62, c1_rho_critical=0.0134, omega_m_fid=0.3, use_omega_m_squared_for_c2=False)[source]#

Return normalized TATT coefficients.

These are the coefficients used by PT IA tracers.

c1 is the linear tidal alignment coefficient.

c2 is the quadratic tidal torquing coefficient.

cdelta is the source density weighting coefficient.

Parameters:

  • z (ArrayLike) – Redshift values.

  • growth_factor (ArrayLike) – Linear growth factor evaluated at z.

  • omega_m (float) – Matter density parameter for the cosmology.

  • a1 (float) – Linear tidal alignment amplitude at the pivot redshift.

  • a2 (float) – Quadratic tidal torquing amplitude at the pivot redshift.

  • a1delta (float) – Source density weighting amplitude at the pivot redshift.

  • eta1 (float) – Redshift evolution index for a1.

  • eta2 (float) – Redshift evolution index for a2.

  • eta1delta (float) – Redshift evolution index for a1delta.

  • z_pivot (float) – Pivot redshift.

  • c1_rho_critical (float) – IA normalization constant.

  • omega_m_fid (float) – Fiducial matter density parameter used by one c2 convention.

  • use_omega_m_squared_for_c2 (bool) – If true, use the convention with omega_m ** 2 / omega_m_fid for c2. If false, use the convention with one power of omega_m.

Returns:

Dictionary containing c1, c2, and cdelta arrays.

Raises:

  • ValueError – If any numerical input is not finite.

  • ValueError – If any redshift value is less than or equal to -1.

  • ValueError – If any growth factor value is not positive.

  • ValueError – If omega_m is not positive.

  • ValueError – If omega_m_fid is not positive.

Return type:

dict[str, ndarray[tuple[Any, …], dtype[float64]]]

plima.models.tatt.tatt_pt_biases(z, *, growth_factor, omega_m, a1=1.0, a2=0.0, a1delta=0.0, eta1=0.0, eta2=0.0, eta1delta=0.0, z_pivot=0.62, c1_rho_critical=0.0134, omega_m_fid=0.3, use_omega_m_squared_for_c2=False)[source]#

Return PT bias inputs for a TATT IA tracer.

Parameters:

  • z (ArrayLike) – Redshift values.

  • growth_factor (ArrayLike) – Linear growth factor evaluated at z.

  • omega_m (float) – Matter density parameter for the cosmology.

  • a1 (float) – Linear tidal alignment amplitude at the pivot redshift.

  • a2 (float) – Quadratic tidal torquing amplitude at the pivot redshift.

  • a1delta (float) – Source density weighting amplitude at the pivot redshift.

  • eta1 (float) – Redshift evolution index for a1.

  • eta2 (float) – Redshift evolution index for a2.

  • eta1delta (float) – Redshift evolution index for a1delta.

  • z_pivot (float) – Pivot redshift.

  • c1_rho_critical (float) – IA normalization constant.

  • omega_m_fid (float) – Fiducial matter density parameter used by one c2 convention.

  • use_omega_m_squared_for_c2 (bool) – If true, use the convention with omega_m ** 2 / omega_m_fid for c2.

Returns:

Dictionary with c1, c2, and cdelta entries. Each entry is a (z, coefficient) tuple.

Return type:

dict[str, tuple[ndarray[tuple[Any, …], dtype[float64]], ndarray[tuple[Any, …], dtype[float64]]]]

plima.models.tatt.unity_ia_bias(z)[source]#

Return a unity IA bias tuple.

This is useful when the IA amplitudes are already included at the PT power spectrum level.

Parameters:

z (ArrayLike) – Redshift values.

Returns:

Tuple (z, ones).

Raises:

  • ValueError – If any redshift value is not finite.

  • ValueError – If any redshift value is less than or equal to -1.

Return type:

tuple[ndarray[tuple[Any, …], dtype[float64]], ndarray[tuple[Any, …], dtype[float64]]]