lyaforecast

Tools to forecast cosmological constraints from Lyman alpha surveys

A collaborative code to predict BAO uncertainties as a function of survey characteristics (area, density of lines of sight, signal to noise...). Eventually, we will be able to forecast constraints from P1D as well.

Some basic ingredients include:

• Spectrograph defined in py/spectrograph.py, currently reads DESI files from desihub to compute expected noise in pixel as a function of quasar magnitude, redshift, and wavelength.
• Survey specifications (area, lmin, lmax...) can be found in lyaforecast/survey.py, the setting will likely change soon. Defaults to DESI numbers.
• The quasar/LBG dn/dz is read from file in lyaforecast/survey.py, currently only compatible with DESI formatting.
• Simple analytical approximation to P1D(z,k) to estimate the aliasing noise, from Palanque-Delabrouille et al. (2013)
• Simple analytical (or CAMB + McDonald 2003 based) code to estimate flux P3D(z,k,mu) to estimate the signal.
• Covariances of multiple correlations estimated in lyaforecast/covariance.py.
• Weights to estimate covariances stored in lyaforecast/weights.py.
• Control module for forecasting lyaforecast/forecast.py

Future plans:

• Estimate covariance between cross- and auto-correlation.
• Improve functionality of code, including information for other surveys.
• Forecast P1D.

Required libraries:

• numpy
• scipy
• camb (Python module for CAMB)