Chicago-2016: r Forecasting
From CMB-S4 wiki
Next-generation "r" forecast planning
Moderator: Raphael Flauger
Status Updates
- David Alonso (8 min) File:SimulatedR DavidAlonso.pdf
- Simulations with PySM
- Basics: Lensed CMB, sync, 1-component dust
- More complicated options: 2-component dust, polarized AME
- Foregrounds based on templates, but add Gaussian small-scale power
- Map-based foreground cleaning
- Fit for foreground spectral indices, amplitudes
- Power spectrum and parameter estimation
- Gaussian likelihood, ell > 30
- Explored non-flat noise, parametrized with ell_knee.
- Need to keep ell_knee in the [10,100] range.
- 2-component dust doesn't bias r strongly (when analyzed assuming 1-component)
- AME polarized at 2% can cause significant bias.
- Simulations with PySM
- Victor Buza (8 min) File:Perfomance based sigma(r) projections.pdf
- Fisher optimization framework -- spectral-based, parametrized foreground model
- Dust and synchrotron foreground model, includes dust decorrelation parameter
- Choose eight observing frequencies for CMB-S4 plus "delensing" band
- Bandpower covariance is derived from BICEP/Keck performance, includes reality factors from detector yield, atmosphere, etc.
- Optimization distributes effort between various frequency bands to get best sigma(r)
- Generated strawman survey definition that has been used for projections by various groups.
- Heterogenous survey with 500k detectors x 4 years, divided between deep and wide surveys.
- Deep survey has low-ell component for r and high-ell component for delensing. Delensing effort is about 30% of total.
- Work in progress
- Need to vary foreground amplitude with fsky -- currently assumes BICEP-level foregrounds at all fsky.
- Add foreground residuals to account for imperfect modeling.
- Map-based mock trials
- Can provide N_ell, f_sky(ell) curves for survey definition.
- Validation of component separation frameworks via these maps.
- Fisher optimization framework -- spectral-based, parametrized foreground model
- Josquin Errard File:Forecast complex fgs.pdf
- Updated forecasting framework. Now includes semi-analytic estimates of bias due to mismodeling.
- Blake Sherwin (8min) File:Delensing residuals.pdf
- Delensing reduces sample variance, but also changes the bias in C_l^BB due to lensing power.
- What if we don't accurately understand the mean lensing residual after delensing? -> leads to bias in r.
- Biases in the lensing reconstruction can be multiplicative or additive.
- Pessimistic case: added 5% biases randomly from ell-to-ell
- Encouraging result: residuals are small and don't look like r.
- Would be good to have a parametrization of bias to marginalize over.
- Delensing reduces sample variance, but also changes the bias in C_l^BB due to lensing power.
Going Forward
- Cross-checks of frameworks with map-based mocks
- Model of foregrounds
- Noise model
- Systematic effects
- How do we interface with instrumentation working groups (and are we ready)?
- Make sure that they are getting latest results.
- Do we need additional justification for splitting atmospheric windows.
- How do we coordinate with non-r forecasts?
- Can we get groups other than BICEP to release noise curves? (Please?)
- Get agreement on how to specify detector-years. Use actual deployed detectors (not good yield) and actual calendar time (not just data passing cuts).
- Are there updates on sky models?
- PSM v2.0 will become public very soon.
- PySM new version
- Boulanger/Ghosh dust model: 3D, multi-layer
- Delensing and foregrounds, masking, etc.
- Model for how high-ell errors in lensing reconstruction turn into low-ell residuals. Then can better fold delensing into optimization.