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DEM Inversion Methods
Will Barnes edited this page Aug 6, 2019
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The table below is a (probably not comprehensive) list of the different methods for computing the differential emission measure.
| Name | Reference | Notes | Source code |
|---|---|---|---|
| MCMC | Vinay & Kashyap (1998) | Easily leverage many Python MCMC implementations | PINTofALE |
| demreg | Hannah & Kontar (2012) | https://github.com/ianan/demreg | |
| sparse AIA | Cheung et al. (2015) | http://www.lmsal.com/~cheung/AIA/tutorial_dem/ | |
| sparse Bayesian | Warren et al. (2017) | ||
xrt_dem_iterative2.pro |
XRT DEM model. Does not seem to have a reference in literature? | SSW | |
| Fast DEM Inversion | Plowman et al. (2012) | http://solar.physics.montana.edu/plowman/firdems.tgz | |
| Pottasch method | Pottasch (1963) | ||
| EM loci method | see Veck et al. (1984) | Find loci of curves defined by ratio of intensity to contribution function; these are upper limits for DEM | |
| A. Leonard Python method | Leonard and Morgan (2014) | In Python | https://github.com/SolarDrew/CoronaTemps |
| DeepEM | Wright et al. (2019) | NN trained on sparse basis pursuit with PyTorch | https://github.com/PaulJWright/DeepEM |
Also see Aschwanden et al. (2015) for a comparison of many of these methods as well as section 7 of Del Zanna and Mason (2018) for a brief overview of some methods.