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)