Simplified lyman-alpha forecast to study in first approximation the effect of increasing the S/N or QSO densities.
Only the Lya auto-correlation is implemented for now.
./forecast.py --snr-filename lya-snr-fuji-sv3.fits --pk-filename PlanckDR16.fits -o forecast-fuji.csv
Use omegam= 0.314569514863487
Do not include non qso targets in SNR
zbins= [2.12 2.28 2.43 2.59 2.75 2.91 3.07 3.23 3.39 3.55]
dndz= [96. 81. 65. 52. 40. 30. 22. 16. 11. 7.]
ntot= 66.39/deg2
2.12 0.06851354023685875 0.07873611435919758 -0.4180956011537333
2.28 0.03584571624530025 0.03799820778882162 -0.4247056553986711
2.43 0.027084143226904736 0.027427243743479296 -0.4280465438556632
2.59 0.02652948694644188 0.026954400035999217 -0.43070126242221085
2.75 0.028427427279722823 0.02962754633603278 -0.4332284987006829
2.91 0.032584086141334075 0.03531044444070297 -0.43573485114126576
3.07 0.03869069966257442 0.04370471853469705 -0.43819909890146075
3.23 0.04562924185925206 0.05338549564062054 -0.44060371422571454
3.39 0.05969687537473999 0.07301863610224386 -0.4429071390331959
3.55 0.08191806395537313 0.10434670521060206 -0.4450852264311003
sigma_log_dh_tot = 0.01152198656616991
sigma_log_da_tot = 0.012258470437353444
The result depend on the snr (or g-band magnitude) of QSO in the sample, so it's different for the Guadalupe data set.
./forecast.py --snr-filename lya-snr-guadalupe.fits --pk-filename PlanckDR16.fits -o forecast-guadalupe.csv
sigma_log_dh_tot = 0.012156286570903056
sigma_log_da_tot = 0.013077418768388797