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. Note that here we assumed (wrongly) that the noise levels quoted by Victor were in intensity, and that the levels in polarization were obtained by multiplying by ${\sqrt {2}}$. 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.