Three-dimensional modeling of chromospheric spectral lines in a simulated active region

Abstract

Context. Because of the complex physics that governs the formation of chromospheric lines, interpretation of solar chromospheric observations is difficult. The origin and characteristics of many chromospheric features are, because of this, unresolved. Aims. We focus here on studying two prominent features: long fibrils and flare ribbons. To model them, we use a 3D MHD simulation of an active region which self-consistently reproduces both of them. Methods. We model the Hα, Mg ii k, Ca ii K, and Ca ii 8542 Å lines using the 3D non-LTE radiative transfer code Multi3D. To obtain non-LTE electron densities, we solve the statistical equilibrium equations for hydrogen simultaneously with the charge conservation equation. We treat the Ca ii K and Mg ii k lines with partially coherent scattering.Results. This simulation reproduces long fibrils that span between the opposite-polarity sunspots and go up to 4 Mm in height. They can be traced in all lines due to density corrugation. Opposite to previous studies, Hα, Mg ii h&k, and Ca ii H&K, are formed at similar height in this model. Although, some of the high fibrils are also visible in the Ca ii 8542 Å line, this line tend to sample loops and shocks lower in the chromosphere. Magnetic field lines are aligned with the Hα fibrils, but the latter holds to a lesser extent for the Ca ii 8542 Å line. The simulation shows structures in the Hα line core that look like flare ribbons. The emission in the ribbons is caused by a dense chromosphere and a transition region at high column mass. The ribbons are visible in all chromospheric lines, but least prominent in Ca ii 8542 Å line. In some pixels, broad asymmetric profiles with a single emission peak are produced, similar to the profiles observed in flare ribbons. They are caused by a deep onset of the chromospheric temperature rise and large velocity gradients. Conclusions. The simulation produces long fibrils similar to what is seen in observations. It also produces structures similar to flare ribbons despite the lack of non-thermal electrons in the simulation. The latter suggests that thermal conduction might be a significant agent in transporting flare energy to the chromosphere in addition to non-thermal electrons.

Diagnosing chromospheric magnetic field through simultaneous spectropolarimetry in Hα and Ca II 854.2 nm

“…However, Leenaarts et al (2012) have shown through numerical simulation that solving 3D radiative transfer equation is essential to model H α line and shown that it has a good sensitivity to chromospheric magnetic field. Very recently Bjørgen et al (2019) have shown again through simulation that flare ribbons are seen in all chromospheric lines including H α but least visible in CaIR.…”

“…The He i samples mostly upper chromospheric layers and it's formation depends on the coronal EUV emission (Andretta & Jones 1997). On the other hand CaIR line gets completely ionized during flares (Kerr et al 2016;Kuridze et al 2018;Bjørgen et al 2019). In order to probe wide range of dynamics those take place in the chromosphere, multi-line spectropolarimetry is needed.…”

“…In order to probe wide range of dynamics those take place in the chromosphere, multi-line spectropolarimetry is needed. In this context H α may play an important role in probing chromospheric magnetic field as it's ubiquitously present in the wide variety of chromospheric plasma conditions such as quiet sun, active regions (Rutten 2007(Rutten , 2008 and flaring regions (Bjørgen et al 2019).…”

The Daniel K. Inouye Solar Telescope – Observatory Overview

“…Similarly, modeling and numerical simulations of chromospheric (Bjørgen et al, 2019) and coronal magnetic-field structures (Rempel, 2017) have advanced continuously and significantly during recent years. Examples include modeling of flares (Cheung et al, 2019) and coronal mass ejections (CMEs) (Fan and Gibson, 2004;Gibson and Fan, 2006;Fan, Gibson, and Tomczyk, 2018).…”

The Lightweaver Framework for Nonlocal Thermal Equilibrium Radiative Transfer in Python

“…The MALI technique assumes that the electron density is known a priori, but this is often not the case. Assuming that the electron density can be given by the LTE ionization state of the plasma can yield substantially incorrect results for chromospheric and prominence lines (Heinzel 1995;Paletou 1995;Bjørgen et al 2019). An additional iteration process is therefore needed to determine the correct electron density within the framework of the NLTE problem.…”