Forecasting

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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

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?



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