ForecastingSims

Simulations for forecasting r performance

Here is a selection of codes and sim maps:

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PySM

Ben Thorne, David Alonso, Jo Dunkley

https://github.com/bthorne93/PySM

Spits out Nside=256 fits maps by running 'python main.py main_config.ini'. See the readme for any required dependencies.

Code already includes a few options for each component, and can be easily extended to add new inputs.

Suggested starting points and ini files for each (noise level to be adjusted):

• over-simple 's1d1a1' = power-law synchrotron, single component dust, minimal spatial variation of indices, no polarised AME
• towards more realism 's4d3a2' = spatial steepening of the synchrotron index away from plane and curvature, 1 component dust with spatially varying dust index sigma=0.3, 2% polarised AME.
• towards more realism 's4d4a2' = spatial steepening of the synchrotron index away from plane and curvature, 2 component dust, 2% polarised AME

Current issues: The version of code on github (v0.3) doesn't yet have small-scale realizations added, so the polarization in low S/N regions is still noisy and all maps are smoothed to 1 degree resolution. This means that power spectra of e.g. the B2 region are noisy and so Victor's method using power spectra won't instantly work.

Solution: could try Victor's method on higher S/N regions as a test of how well things work. Or wait for the small-scale Gaussian sims (~2 weeks). Pixel-based methods will work a little better on these current v0.3 sims.

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