Forecasting

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Forecasting non-r parameters

The expectation is that these forecasts will be done mainly using Fisher forecasts for the Science Book, with prudent use of frequencies and ell-range to avoid being over-optimistic about the impact of foregrounds and noise from the ground.


Parameters

Names of people responsible for generating forecasts in brackets. Sign yourself up and we can divide up tasks when we speak if there are multiple people.

  • LCDM 6-parameters


  • curvature
  • running
  • birefringence
  • correlated isocurvature amplitude
  • uncorrelated isocurvature amplitude
  • cosmic string tension
  • primordial magnetic fields


  • neutrino mass sum (Mat)
  • Neff (Dan G?)
  • Yp
  • neutrino sound speed


  • dark matter annihilation (Cora)
  • dark matter interactions
  • utralight-axion density (Renee)
  • w
  • w0,wa
  • f

Here is place-holder for some suggested fiducial parameters and step-sizes, can be different though.


Fisher codes

With info on what data they can handle and who is available to run them during next two months

  • Errard/Feeney code (Josquin Errard)
  • Allison et al code - primary plus lensing plus BAO (Danielle Leonard, Jo Dunkley)
  • Extension of Allison et al code for axions (Renee Hlozek)
  • Madhavacheril's code (similar to Allison et al) (Mat Madhavacheril)
  • de Bernardis code - includes kSZ likelihood (Francesco de Bernardis)
  • who has a tSZ likelihood?
  • other codes and people?

Settings

  • S4 TT/TE/EE/kk over 40% of sky, 30<ell < lmax
  • Planck TT/TE/EE from 30<l<2500 over additional 20% of sky. Use these 'Planck-pol' specs for noise:File:Planck pol.pdf
  • Planck TT at l<30 over 80% of sky
  • Tau prior 0.06+-0.01


  • lmax(TT)=3000 unless explicit foreground cleaning is done in code for kSZ etc
  • lmax(TE,EE)=5000 unless explicit foreground cleaning done in code
  • kk reconstructed from 30<l<lmax using MV estimate


  • quadratic estimator for lensing, ideally with iterative delensing
  • Gaussian likelihood neglecting T/E/k covariance is ok, but non-Gaussian better (do any codes have full T/E/B/k covariance?)
  • non-linear power spectrum for kk, e.g. ok to use halofit in CAMB


  • cluster masses calibrated with LSST lensing (need to refine what that means)

Specs

Nominal test case

  • Single channel (e.g. 150 GHz) at 1 uK/amin in T and 1.4uK/amin in P, 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