Efficient and automated inversions of magnetically sensitive forbidden coronal lines: CLEDB – the coronal line emission database magnetic field inversion algorithm

We present CLEDB, a "single-point inversion" algorithm for inferring parameters using IStokes parameters of forbidden magnetic dipole lines formed in the solar corona. We select lines of interest and construct databases of Stokes parameters for combinations of plasma thermal and magnetic configurations. The size and complexity of such databases are drastically reduced by taking advantage of symmetries. Using wavelength-integrated line profiles, each of which might be decomposed beforehand into several line-of-sight components, we search for nearest matches to observed Stokes parameters computed for the elongation corresponding to the observed region. The method is intended to be applied to two or more lines observed simultaneously. The solutions initially yield magnetic orientation, thermal properties, and the spatial position of the emitting plasma in three dimensions. Multiple possible solutions for each observation are returned, including irreducible degeneracies, where usually sets of two solutions are compatible with the two input Imeasurements. In solving for the scattering geometry, this method avoids an additional degeneracy pointed out by Dima and Schad (Astrophys. J.889, 109, 2020). The magnetic field strength is separately derived from the simple ratio of observed to database Stokes V data, after the thermal properties and scattering geometry solutions have been determined.

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Author Paraschiv, Alin Razvan
Judge, Philip Gordon
Publisher UCAR/NCAR - Library
Publication Date 2022-05-25T00:00:00
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Metadata Date 2023-08-18T18:36:31.831747
Metadata Record Identifier edu.ucar.opensky::articles:25456
Metadata Language eng; USA
Suggested Citation Paraschiv, Alin Razvan, Judge, Philip Gordon. (2022). Efficient and automated inversions of magnetically sensitive forbidden coronal lines: CLEDB – the coronal line emission database magnetic field inversion algorithm. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d7qn6bhb. Accessed 20 June 2025.

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