Welcome to CosmoWAP Documentation

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

CosmoWAP is an effort to provide a (hopefully) self-consistent framework to compute contributions to the Fourier power spectrum and bispectrum from wide-separation and relativistic effects as well as contributions from Primordial non-Gaussianity (PNG).

These expressions can be very cumbersome, and it can be tricky to check for consistency in the community, so hopefully this code should be useful in that regard.

CosmoWAP is a Python package for analyzing the power spectra and bispectra, but the analytical expressions themselves are computed in Mathematica using routines which are publicly available at the repository MathWAP. These expressions are then exported as .py files. Therefore, the main functionality of CosmoWAP is to take these expressions and implement them for a given cosmology (from CLASS) and survey parameters.

Getting Started:

• Installation
  • Installing via pip
  • Development Version
  • Optional: CosmoPower
• Getting Started with CosmoWAP
  • 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
  • Usage
• Bias modelling
  • PBBias
  • Usage Example
  • HMF

Power Spectra and Bispectra:

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

Forecasting:

• 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