pyccl.nl_pt.tracers module

pyccl.nl_pt.tracers.translate_IA_norm(cosmo, *, z, a1=1.0, a1delta=None, a2=None, Om_m2_for_c2=False, Om_m_fid=0.3)[source]

Function to convert from \(A_{ia}\) values to \(c_{ia}\) values, for the intrinsic alignment bias parameters using the standard convention of Blazek et al. 2019 or the variant used by the Dark Energy Survey analysis.

Parameters:

  • cosmo (Cosmology) – cosmology object.

  • z (float or array) – z value(s) where amplitude is evaluated.

  • a1 (float or array) – IA \(A_1\) at input z values.

  • a1delta (float or array) – IA \(A_{1\delta}\) at input z values.

  • a2 (float or array) – IA \(A_2\) at input z values.

  • Om_m2_for_c2 (bool) – True to use the Blazek et al. 2019 convention of \(\Omega_m^2\) scaling.

  • Om_m_fid (float) – Value for Blazek et al. 2019 scaling.

Returns:

Tuple of IA bias parameters

  • c1 (float or array): IA \(C_1\) at input z values.

  • c1delta (float or array): IA \(C_{1\delta}\) at input z values.

  • c2 (float or array): IA \(C_2\) at input z values.

class pyccl.nl_pt.tracers.PTTracer(self)[source]

Bases: CCLAutoRepr

PTTracers contain the information necessary to describe the perturbative, non-linear inhomogeneities associated with different physical quantities.

In essence their main function is to store a set of redshift-dependent functions (e.g. perturbation theory biases) needed in a perturbation theory framework to provide N-point correlations.

abstract property type

String defining tracer type ('M', 'NC' and 'IA' supported).

get_bias(bias_name, z)[source]

Get the value of one of the bias functions at a given redshift.

Parameters:

  • bias_name (str) – name of the bias function to return.

  • z (float or array) – redshift.

Returns:

bias value at the input redshifts.

Return type:

(float or array)

class pyccl.nl_pt.tracers.PTMatterTracer(self)[source]

Bases: PTTracer

PTTracer representing matter fluctuations.

type = 'M'
class pyccl.nl_pt.tracers.PTNumberCountsTracer(self, b1, b2=None, bs=None, b3nl=None, bk2=None)[source]

Bases: PTTracer

PTTracer representing number count fluctuations. This is described by 1st and 2nd-order biases and a tidal field bias. These are provided as floating point numbers or tuples of (reshift,bias) arrays. If a number is provided, a constant bias is assumed. If None, a bias of zero is assumed.

Parameters:

  • b1 (float or tuple) – a single number or a tuple of arrays (z, b(z)) giving the first-order bias.

  • b2 (float or tuple) – as above for the second-order bias.

  • bs (float or tuple) – as above for the tidal bias.

  • b3nl (float or tuple) – as above for the third-order bias.

  • bk2 (float or tuple) – as above for the non-local bias.

type = 'NC'
property b1

Internal first-order bias function.

property b2

Internal second-order bias function.

property bs

Internal tidal bias function.

property b3nl

Internal third-order bias function.

property bk2

Internal non-local bias function.

class pyccl.nl_pt.tracers.PTIntrinsicAlignmentTracer(self, c1, c2=None, cdelta=None)[source]

Bases: PTTracer

PTTracer representing intrinsic alignments. This is described by 1st and 2nd-order alignment biases and an overdensity bias. These are provided as floating point numbers or tuples of (reshift,bias) arrays. If a number is provided, a constant bias is assumed. If None, a bias of zero is assumed.

Parameters:

  • c1 (float or tuple) – a single number or a tuple of arrays (z, c1(z)) giving the first-order alignment bias \(C_1\).

  • c2 (float or tuple) – as above for the second-order alignment bias \(C_2\).

  • cdelta (float or tuple) – as above for the overdensity bias \(C_{1\delta}\).

type = 'IA'
property c1

Internal first-order bias function.

property c2

Internal second-order bias function.

property cdelta

Internal overdensity bias function.