Welcome to CosmoWAP Documentation

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COSMOlogy with Wide-separation, relAtivistic and Primordial non-gaussian contributions

CosmoWAP is a Python framework for forecasts of large-scale galaxy clustering using 3D power spectrum and/or bispectrum multipoles. The modelling covers relativistic effects, wide-separation corrections, and Primordial non-Gaussianity (PNG) in standard perturbation theory.

The core analytical expressions are derived in Mathematica using MathWAP and exported as .py files. These expressions take a cosmology (via CLASS) and a set of bias models as input. Built around this core is the forecasting machinery: bias modelling with HOD/luminosity functions, theoretical covariances, Fisher matrices, and MCMC modules.

Getting Started:

• Installation
  • Installing via pip
  • Development Version
  • Optional: CosmoPower
• Getting Started with CosmoWAP
  • Code Structure
  • Basic Setup
  • Computing Power Spectra
  • Computing Bispectra
  • Forecasting with Fisher Matrices
• Example Notebooks

Biases:

• Survey parameters
  • Preset Surveys
  • Survey Attributes
  • Basic Usage
  • Multi-Tracer Analysis
  • Bright-Faint Split
  • Custom Surveys
  • See Also
• Luminosity functions
  • Hα Luminosity Functions
  • Magnitude-Limited Surveys (k-correction)
  • Usage
• Bias modelling
  • HOD Models
  • HMF
  • PBBias
  • Usage Example
  • Extending

Power Spectra and Bispectra:

• ClassWAP
  • Core Class
  • Cosmological Functions
  • Survey and Bias Parameters
  • Example Usage
• Power Spectrum
  • Methods
  • Usage
  • Numerical \(\mu\) Pipeline
• Integrated Effects
  • Integration Methods
  • Analytic \(\mu\) Pipeline
  • Numerical \(\mu\) Pipeline
  • Analytic Multipole Classes
• Bispectrum
  • Bispectrum Multipoles
  • Available Bispectrum Classes
  • Full Local Bispectrum
  • Bispectrum Gaussian Covariance

Forecasting:

• Gaussian Covariance
  • Power Spectrum Covariance
  • Bispectrum Covariance
  • Multi-Tracer Covariance
  • Nonlinear Corrections
  • Comparison with Simulations
  • Analytical Expressions
• Forecasting
  • FullForecast
  • FisherMat
  • FisherList
  • PkForecast / BkForecast
  • Sampler
• SNR
  • Single-Bin vs Full Forecast
  • Using get_fish for SNR
  • Dedicated SNR Methods
  • Single-Bin Access
• Best-fit Bias
  • Method
  • Usage
  • Alternative: best_fit_bias

MathWAP:

• Overview
• Kernels
• Power Spectrum
• Bispectrum

Reference:

• Citation

Indices and tables

• Index

• Module Index

• Search Page