Differential Emission Measures from the Regularized Inversion of Hinode and SDO data

By I. Hannah, E. P. Kontar. 2012

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Aims. To demonstrate the capabilities of regularized inversion to recover differential emission measures (DEMs) from multiwavelength observations provided by telescopes such as Hinode and SDO. Methods. We develop and apply an enhanced regularization algorithm, used in RHESSI X-ray spectral analysis, to constrain the ill-posed inverse problem that is determining the DEM from solar observations. We demonstrate this computationally fast technique applied to a range of DEM models simulating broadband imaging data from SDO/AIA and high resolution line spectra from Hinode/EIS, as well as actual active region observations with Hinode/EIS and XRT. As this regularization method naturally provides both vertical and horizontal (temperature resolution) error bars we are able to test the role of uncertainties in the data and response functions. Results. The regularization method is able to successfully recover the DEM from simulated data of a variety of model DEMs (single Gaussian, multiple Gaussians and CHIANTI DEM models). It is able to do this, at best, to over four orders of magnitude in DEM space but typically over two orders of magnitude from peak emission. The combination of horizontal and vertical error bars and the regularized solution matrix allows us to easily determine the accuracy and robustness of the regularized DEM. We find that the typical range for the horizontal errors is ΔlogT ≈ 0.1−0.5 and this is dependent on the observed signal to noise, uncertainty in the response functions as well as the source model and temperature. With Hinode/EIS an uncertainty of 20% greatly broadens the regularized DEMs for both Gaussian and CHIANTI models although information about the underlying DEMs is still recoverable. When applied to real active region observations with Hinode/EIS and XRT the regularization method is able to recover a DEM similar to that found via a MCMC method but in considerably less computational time. Conclusions. Regularized inversion quickly determines the DEM from solar observations and provides reliable error estimates (both horizontal and vertical) which allows the temperature spread of coronal plasma to be robustly quantified.

Subfields: Astronomy and Astrophysics, Molecular Biology, Radiation

View Paper: https://www.semanticscholar.org/paper/70e3d5efdafec344961c6699dc0fd2b99c61725c

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Citations in the corpus

Observations of Turbulent Magnetic Reconnection within a Solar Current Sheet (2018, methodology): “We also take advantage of other two inversion methods independently developed by Hannah & Kontar (2012) and Cheung et al. (2015), respectively.”
THERMAL DIAGNOSTICS WITH THE ATMOSPHERIC IMAGING ASSEMBLY ON BOARD THE SOLAR DYNAMICS OBSERVATORY: A VALIDATED METHOD FOR DIFFERENTIAL EMISSION MEASURE INVERSIONS (2015, methodology): “Log-normal distributions are commonly chosen to serve as test cases for inversion codes (Guennou et al. 2012a, 2012b; Hannah & Kontar 2012; Plowman et al. 2013) and as functional forms for DEM inversions of AIA data (e.g., Aschwanden & Boerner 2011).”
CHIANTI - An atomic database for Emission Lines. Version 8 (2015, methodology): “As an option, the routine can also run DATA2DEM_REG, a routine written by I. Hannah (described in Hannah & Kontar 2012) and/or the Markov-Chain Monte-Carlo algorithm MCMC_DEM, written by V. Kayshap and described in Kashyap & Drake (1998).”
The multi-thermal emission in solar active regions (2013, methodology): “The Hannah & Kontar (2012) code performed relatively well but in places failed completely to provide an acceptable representation of the observations.”