Monday, April 06, 2015

Sparse inversion of Stokes profiles. I. Two-dimensional Milne-Eddington inversions


Sparse inversion of Stokes profiles. I. Two-dimensional Milne-Eddington inversions by A. Asensio Ramos , J. de la Cruz Rodriguez
Inversion codes are numerical tools used for the inference of physical properties from the observations. Despite their success, the quality of current spectropolarimetric observations and those expected in the near future presents a challenge to current inversion codes. The pixel-by-pixel strategy of inverting spectropolarimetric data that we currently utilize needs to be surpassed and improved. The inverted physical parameters have to take into account the spatial correlation that is present in the data and that contains valuable physical information. We utilize the concept of sparsity or compressibility to develop an new generation of inversion codes for the Stokes parameters. The inversion code uses numerical optimization techniques based on the idea of proximal algorithms to impose sparsity. In so doing, we allow for the first time to exploit the presence of spatial correlation on the maps of physical parameters. Sparsity also regularizes the solution by reducing the number of unknowns. We compare the results of the new inversion code with pixel-by-pixel inversions, demonstrating the increase in robustness of the solution. We also show how the method can easily compensate for the effect of the telescope point spread function, producing solutions with an enhanced contrast.
thank you Andrés !
Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

No comments: