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DEM Inversion Methods
Will Barnes edited this page Apr 24, 2020
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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 |
Weber et al. (2004), Golub et al. (2004) | SSW; https://hesperia.gsfc.nasa.gov/ssw/hinode/xrt/idl/util/xrt_dem_iterative2.pro | |
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 |