MapBasedR

(David Alonso writing)

Summary

We checked the $f_{sky}=0.1$ case in Victor's forecasts using our map-based component separation code on PySM simulations. Bullet-point results (details below):

• Assuming no delensing: $\sigma (r)=1.6\times 10^{-3}$
• Assuming a 0.25 delensing factor: $\sigma (r)=0.84\times 10^{-3}$

These number roughly agree with Victor's forecast $(\sigma (r)=0.95\times 10^{-3})$, considering our potentially different assumptions about noise power spectra and delensing efficiency.

Simulations

We used PySM to generate full-sky simulations including:

• Power-law synchrotron (spatially-varying spectral index).
• Single-component thermal dust (spatially-varying temperature and spectral index).
• $r=0$, partially de-lensed CMB.
• Noise levels compatible with Victor's $f_{sky}=0.1$ case.

The maps are cut using a mask defined by selecting the cleanest 4000 sq-deg of the southern sky in polarization.

Foreground removal

We run a map-based Bayesian component-separation code (Alonso et al. in prep.) on the simulations. The code samples the fully-resolved amplitudes of the three different components as well as spectral parameters ($\beta _{s},\,\beta _{d}$ and $T_{d}$). The latter are assumed constant on larger pixels, with HEALPix resolution $N_{\rm {side}}=16$ (corresponding to ~4 deg). Fig. below shows the B-mode map at 145 GHz (left) and the mean CMB-only B-mode map output by the code (right).

Estimating r

We compute the B-mode power spectrum for each simulation and fit a primordial + lensing $C_{\ell }$ template with amplitudes for both components (the first one being $r$). For this we only use multipoles $\ell >30$. See result below for the no-delensing and 0.25-delensing cases.