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The general objective of this project is to study different schemes to account for the mode coupling effect resulting from partial observations of the sky.
Description:
At the moment we measure the pseudo-cls and the mode coupling matrix from the data. Then the mode coupling matrix is convolved with the theory (full sky) power spectrum to match observations. However, one can imagine that the inverse is also possible; i.e. deconvolved the meausured power spectrum with the mode coupling matrix to obtain a estimate of what the associated full sky power spectrum would be and match that to the theoretical expectations. In this project we want to investigate which is the best way to go about this in different scenarios.
Mile stones:
Install 'gitd' Python environment
Run example notebook (In NERSC)
Implement the deconvolution of the power spectrum in Heracles
Notes
There seems to be a problem when using healpy to load NSIDE=8K partial maps resulting in jupyter's kernel crashing. The problem only seems to manifest in WSL machines.
The text was updated successfully, but these errors were encountered:
Deconvolving measured partial Cls
Goal:
The general objective of this project is to study different schemes to account for the mode coupling effect resulting from partial observations of the sky.
Description:
At the moment we measure the pseudo-cls and the mode coupling matrix from the data. Then the mode coupling matrix is convolved with the theory (full sky) power spectrum to match observations. However, one can imagine that the inverse is also possible; i.e. deconvolved the meausured power spectrum with the mode coupling matrix to obtain a estimate of what the associated full sky power spectrum would be and match that to the theoretical expectations. In this project we want to investigate which is the best way to go about this in different scenarios.
Mile stones:
Notes
The text was updated successfully, but these errors were encountered: