Survey Parameters

The SurveyParams class defines survey-specific parameters for use in CosmoWAP. It allows for all survey specfic information to be fed into ClassWAP. This module contains predefined parameters for several surveys, as well as functionality to customise specifications and create multi-tracer samples. The preset surveys are taken from exisitng literature and used in the forecasts in arxiv:2407.00168 and arxiv:2511.09466 (See survey details section for more information)

It is simplie to define a new survey - one only needs to define linear bias model and a luminosity fucntion (/adopt an exisiting one with a given flux/magnitude cut or alternateively one can define all biases as a redshift dependent fucntion). From this we can compute evolution and magnification biases. Second order and PNG scale-dependent biases can then be computed from a given HOD and HMF or can simply be defined as a redshift dependent function.

Preset Surveys

class SurveyParams.Euclid(cosmo, fitting=False, model3=True, F_c=None)

Euclid Hα galaxy survey (0.9 < z < 1.8).

Parameters:

  • cosmo – CLASS cosmology instance

  • fitting (bool) – Use polynomial fits instead of luminosity function (default: False)

  • model3 (bool) – Use Model 3 luminosity function (default: True)

  • F_c (float) – Flux cut [erg/cm²/s] (default: 2e-16 for model3, 3e-16 for model1)

class SurveyParams.Roman(cosmo, model3=False, F_c=None)

Roman Space Telescope Hα survey (0.5 < z < 2.0).

class SurveyParams.BGS(cosmo, m_c=20, fitting=False)

DESI Bright Galaxy Sample (0.05 < z < 0.6).

Parameters:

m_c (float) – Apparent magnitude cut (default: 20)

class SurveyParams.MegaMapper(cosmo, m_c=24.5)

MegaMapper LBG survey (2.1 < z < 5.0).

Parameters:

m_c (float) – Apparent magnitude cut (default: 24.5)

class SurveyParams.SKAO1(cosmo)

SKA Observatory Phase 1 HI galaxy survey.

class SurveyParams.SKAO2(cosmo)

SKA Observatory Phase 2 HI galaxy survey.

class SurveyParams.DM_part(cosmo)

Dark matter particles (b₁ = 1, for testing).

Survey Attributes

Each survey class provides (either as redshift dependent functions or scalars):

  • b_1: Linear bias b₁(z)

  • z_range: Redshift range [z_min, z_max]

  • be: Evolution bias bₑ(z)

  • Q: Magnification bias Q(z)

  • n_g: Number density n_g(z) [h³/Mpc³]

  • f_sky: Sky fraction

  • LF: Luminosity function object (if defined)

  • b_2, g_2, loc.b_01, eq.b_11: Optional second-order and PNG biases (if defined or computed via PBBias)

Basic Usage

import cosmo_wap as cw
from cosmo_wap.lib import utils

cosmo = utils.get_cosmo()

# Single tracer LBG MegaMapper-like survey
survey = cw.SurveyParams.MegaMapper(cosmo)
cosmo_funcs = cw.ClassWAP(cosmo, survey)

# Access survey attributes
z = 3
print(f"b_1(z=3) = {survey.b_1(z):.2f}")
print(f"n_g(z=3) = {survey.n_g(z):.4f} h³/Mpc³")

# It is simple to modify parameters
survey_half_sky = survey.update(f_sky=0.5)

Multi-Tracer Analysis

Pass a list of survey objects to ClassWAP for multi-tracer analysis:

# Two different surveys (crucially with overlappign redshifts!)
survey_euclid = cw.SurveyParams.Euclid(cosmo)
survey_ska = cw.SurveyParams.SKAO2(cosmo)
cosmo_funcs_mt = cw.ClassWAP(cosmo, [survey_euclid, survey_ska])

# Or bright/faint split of same survey (see below)
bright, faint = cw.SurveyParams.Euclid(cosmo).BF_split(5e-16)
cosmo_funcs_bf = cw.ClassWAP(cosmo, [bright, faint])

Multi-tracer forecasting then accounts for cross-correlations between tracers.

Bright-Faint Split

Surveys with luminosity functions can be split into bright and faint subsamples at a given flux/magnitude cut. This enables multi-tracer analysis from a single survey.

SurveyBase.BF_split(split)

Split survey into bright and faint subsamples.

Parameters:

split (float) – Flux cut [erg/cm²/s] for Hα surveys, or magnitude cut for magnitude-limited surveys. Must be brighter than the survey’s detection limit.

Returns:

List of [bright, faint] survey objects

Note

BF_split is only valid for surveys with a defined luminosity function (e.g. Euclid, Roman, BGS, MegaMapper). Surveys without one (e.g. SKAO, DM_part, custom surveys) will raise a ValueError.

The faint sample parameters are derived from:

  • n_faint = n_total - n_bright

  • b₁_faint = (n_total × b₁_total - n_bright × b₁_bright) / n_faint

# Euclid with default flux cut 2e-16 erg/cm²/s
survey = cw.SurveyParams.Euclid(cosmo)

# Split at brighter flux cut (5e-16)
bright, faint = survey.BF_split(5e-16)

# Check number densities
z = 1.2
print(f"n_bright = {bright.n_g(z):.4f}")
print(f"n_faint = {faint.n_g(z):.4f}")
print(f"n_total = {survey.n_g(z):.4f}")

# Use in multi-tracer forecast
cosmo_funcs = cw.ClassWAP(cosmo, [bright, faint])

Custom Surveys

Create custom survey parameters:

import numpy as np

# Start from template
custom = cw.SurveyParams.InitNew(cosmo)

# Set parameters
custom.b_1 = lambda z: 1.0 + 0.5 * z
custom.z_range = [0.5, 2.0]
custom.n_g = lambda z: 0.01 * np.exp(-z)
custom.Q = lambda z: 0.4 + 0 * z
custom.be = lambda z: 0.0 + 0 * z
custom.f_sky = 0.3

cosmo_funcs = cw.ClassWAP(cosmo, custom)

See Also