Difference between revisions of "ForecastingSims"
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| − | + | Spits out Nside=256 fits maps by running 'python main.py main_config.ini'. See the readme for any required dependencies. | |
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| + | Code already includes a few options for each component, and can be easily extended to add new inputs. | ||
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Suggested starting points and ini files for each: | Suggested starting points and ini files for each: | ||
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====(Other available codes - TBD)==== | ====(Other available codes - TBD)==== | ||
Revision as of 05:59, 13 May 2016
Simulations for forecasting r performance
Here is a selection of codes and sim maps:
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:
- over-simple = single component dust, power-law synchrotron, minimal spatial variation of index, no polarised AME
- towards more realism = 2 component dust, spatial steepening of the synchrotron index away from plane, 2% polarised AME.
- towards more realism = 1 component dust with spatially varying dust index, spatial steepening of the synchrotron index away from plane, 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-scales (~2 weeks). Pixel-based methods will work a little better on these sims.
