Solar plasma temperature diagnostics in flares and active regions from spectral lines in the range 280–330 Å in the SPIRIT/CORONAS-F experiment

Shestov, S. V.; Kuzin, Sergey; Urnov, A. M.; Ulyanov, A. S.; Богачев, С. А.

doi:10.1134/s1063773710010056

Cited by 25 publications

(30 citation statements)

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“…The calculations performed here in numerical tests and for real observable data showed that the decrease in the temperature bins starting from Δ log T = 0.1 leads to some changes in the DEM confidence level, which, however, are negligible relative to the statistical errors in the measurements for both spectral lines and broadband channels (see also Fig. 4 in Shestov et al 2010). In the present paper, we found it reasonable to restrict ourselves to a bin size no smaller than Δ log T = 0.01, since the solution satisfying the ML criterion, as well as its confidence level, do not change when the temperature bin continues to decrease.…”

Section: Probabilistic Approach To the Spectral Inverse Problemmentioning

confidence: 99%

“…The other line set includes the nine spectral lines observed by the SPIRIT spectroheliograph onboard CORONAS-F satellite and used for the DEM analysis of a number of solar ARs in Shestov et al (2010) (hereinafter referred to as the "SPIRIT line set"). The lines derived from EUV spectra in the wavelength range 280−330 Å are Ni XVIII 291.98 Å, Fe XXII 292.46 Å, Si IX 296.11 Å, Ca XVIII 302.19 Å, Si XI 303.33 Å, Fe XX 309.29 Å, Si VIII 319.84 Å, Ni XVIII 320.57 Å, and Fe XVII 323.65 Å (see also Fig.…”

Section: Numerical Tests and Simulationsmentioning

confidence: 99%

“…For this set, the temperature coverage constitutes log T ≈ 5.9−7.2. To be consistent with and Shestov et al (2010) in our DEM analysis, we used the same version of the CHIANTI database (version 5.2.1 - Dere et al 1997;Landi et al 2006) to calculate line emissivities, as well as the same ion abundances (Mazzotta et al 1998) and the coronal element abundances (Feldman et al 1992). …”

Section: Numerical Tests and Simulationsmentioning

confidence: 99%

“…), and the roles of magnetic reconnection and waves in energy release processes. In particular, considerable interest in the detection of hot plasma (T > 5 MK) in coronal loops has arisen (Zhitnik et al 2006;Urnov et al 2007b;Reale et al 2009;Schmelz et al 2009a,b;Patsourakos & Klimchuk 2009;Shestov et al 2010). This finding may be related to a coronal heating mechanism, based on the Parker's idea of nanoflares (Parker 1988).…”

Section: Introductionmentioning

confidence: 99%

“…In Sect. 4, we apply the BIM to the DEM analysis of spectroscopic data from SUMER , SPIRIT (Shestov et al 2010), and XRT (Reale et al 2009) ARs and compare them with other diagnostic techniques. In Sect.…”

Section: Introductionmentioning

confidence: 99%

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An iterative method in a probabilistic approach to the spectral inverse problem

Goryaev

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Urnov

et al. 2010

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Context. Inverse problems are of great importance in astrophysics, e.g., for deriving information about the physical characteristics of hot optically thin plasma sources from their extreme ultraviolet and X-ray spectra. Aims. We describe and test an iterative method developed within the framework of a probabilistic approach to the spectral inverse problem for determining the thermal structures of the emitting plasma. We also demonstrate applications of this method to both high resolution line spectra and broadband imaging data. Methods. Our so-called Bayesian iterative method (BIM) is an iterative procedure based on Bayes' theorem and is used to reconstruct differential emission measure (DEM) distributions. Results. To demonstrate the abilities of the BIM, we performed various numerical tests and model simulations establishing its robustness and usefulness. We then applied the BIM to observable data for several active regions (AR) previously analyzed with other DEM diagnostic techniques: both SUMER/SOHO (Landi & Feldman 2008, ApJ, 672, 674) and SPIRIT/CORONAS-F (Shestov et al. 2010, Astron. Lett., 36, 44) line spectra data, and XRT/Hinode (Reale et al. 2009, ApJ, 698, 756) broadband imaging data. The BIM calculations confirmed the main results for SUMER/SOHO data showing very good quantitative agreement between both DEMs at log T ≈ 6.5 (T is the temperature in units of Kelvin) and a slight shift for two maxima at lower temperatures with ≈30−50% difference in the DEM values for the coolest peak. For the SPIRIT data, we revised and validated AR DEM results including the inference of hot plasma in ARs with an emission measure (EM) at temperatures ≈9−15 MK comparable to the EM at ≈2−4 MK. In the case of XRT broadband data, the BIM solutions provided evidence of hot plasma at temperatures ≈4−6 MK with EM up to ∼30% as compared to that at ≈2−4 MK in a non-flaring AR on 2006 November 12.Conclusions. The BIM results show that this method is an effective tool for determining the thermal structure of emitting plasma and can be successfully used for the DEM analysis of both line spectra and broadband imaging data. The BIM calculations correlate with recent studies confirming the existence of hot plasma in solar ARs. The BIM results also indicate that the coronal plasma may have the continuous distributions predicted by the nanoflare paradigm.

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Section: Probabilistic Approach To the Spectral Inverse Problemmentioning

confidence: 99%

Section: Numerical Tests and Simulationsmentioning

confidence: 99%