Forecasting

Forecasting non-r parameters

Parameters

• LCDM 6-parameters

• curvature
• running
• birefringence
• correlated isocurvature amplitude
• uncorrelated isocurvature amplitude
• cosmic string tension

• neutrino mass sum
• Neff
• Yp
• neutrino sound speed

• dark matter annihilation
• light-axion fraction (Renee Hlozek)
• w
• w0,wa
• f

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Codes =

With info on what data they can handle and who is available to run them

• Errard/Feeney code (Josquin Errard)
• Allison et al code - primary plus lensing plus cross-correlations plus BAO (Danielle Leonard, Jo Dunkley)
• Madhavacheril's code (similar to Allison et al) (Mat Madhavacheril)
• de Bernardis code - includes kSZ likelihood (Francesco de Bernardis)
• tSZ likelihood?

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Settings

• Planck at l<30 over 80% of sky
• S4 TT/TE/EE/kk over 40% of sky, 30<l < lmax
• Planck TT/TE/EE from 30<l<2500 over additional 40% of sky
• Or neglect l<30 TE/EE and use tau prior 0.06+-0.01

• lmax(TT)=3000 unless explicit foreground cleaning is done in code
• lmax(TE,EE)=4000 unless explicit foreground cleaning done in code

• quadratic estimator with iterative delensing
• Gaussian likelihood neglecting T/E/k covariance is ok, but non-Gaussian better (what codes have that?)

Specs

Nominal test case

• Single channel (e.g. 150 GHz) at 1 uK/amin, 3 arcmin resolution.
• White noise, no FG inflation
• Useful if your code can spit out errors as function of noise level in 1-10 uK/arcmin range and resolution in range 1-10 arcmin.

Next steps

• Define multiple frequencies of the survey
• Decide if an N_ell that captures non-white-noise is necessary
• Define residual FG level if any