# Difference between revisions of "Forecasting"

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*Planck TT/TE/EE from 30<l<2500 over additional 40% of sky | *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 | *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(TT)=3000 unless explicit foreground cleaning is done in code | ||

*lmax(TE,EE)=4000 unless explicit foreground cleaning done in code | *lmax(TE,EE)=4000 unless explicit foreground cleaning done in code | ||

+ | |||

*quadratic estimator with iterative delensing | *quadratic estimator with iterative delensing | ||

*Gaussian likelihood neglecting T/E/k covariance is ok, but non-Gaussian better (what codes have that?) | *Gaussian likelihood neglecting T/E/k covariance is ok, but non-Gaussian better (what codes have that?) | ||

+ | |||

*option to add DESI BAO | *option to add DESI BAO |

## Revision as of 08:11, 5 April 2016

### 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

- 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?)

- option to add DESI BAO

#### 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