In this posting I use our spectral-based analysis framework to arrive at parameter
constraints derived from the DC1.0 sim-set, and compare the results to the CMB-S4
science book contraints (which were based on scaling the BICEP/Keck covariance
matrix), as well as a new Fisher calculation which uses a BPCM derived from the
DC1.0 sims.

Notes on the method:

- We use the DC 1.0 sim-set (cmb + dust + sync + noise)
- Use the BICEP/Keck multi-component spectral-based likelihood framework.
- Use a pure-B estimator (J. Grain et al, Phys. Rev. D. 79,12315) to calculate all the auto and cross spectra.
- Use a global ML peak search (of similar dimensionality as in the Science Book Fisher forecasts, bar a dust decorrelation parameter) to obtain recovered ML histograms. The standard deviations of histograms offer a measure of the constraining power in our dataset. The means of histograms offer a measure of bias.

The results of the pager above are summarized in the table below under the DC1.0 column. As mentioned, I compare the results to the CMB-S4 science book contraints, based on scaling the BICEP/Keck covariance matrix (under the Fisher, BK scaled column), and perform a new Fisher calculation which uses a BPCM derived from the DC1.0 sims (Fisher, DC1.0). We expect the DC1.0 constraints to be more optimistic than the Science Book results due to idealized nature of the simulations, but we expect good agreement between the DC1.0 contraints and Fisher DC1.0, which we do see up to sample variance (we only have 70 sims, thus \(\sqrt{2/70}\sigma=0.17 \sigma\))

\(f_{sky}=0.03\) | Fisher (BK scaled) | DC 1.0 | Fisher (DC 1.0) |
---|---|---|---|

\(\sigma_r(r=0, A_L=1.00), \times 10^{-3}\) | 3.82 | 2.61 | 2.63 |

\(\sigma_r(r=0, A_L=0.30), \times 10^{-3}\) | --- | 1.13 | 1.03 |

\(\sigma_r(r=0, A_L=0.10), \times 10^{-3}\) | 0.91 | 0.67 | 0.56 |

\(\sigma_r(r=0, A_L=0.03), \times 10^{-3}\) | --- | 0.46 | 0.38 |