Abstract
Abstract The solar corona consists of a million degree Kelvin plasma. A complete understanding of this phenomenon demands the study of quiet Sun (QS) regions. In this work, we study QS regions in the 171 Å, 193 Å, and 211 Å passbands of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory, by combining the empirical impulsive heating forward model of Pauluhn & Solanki with a machine-learning inversion model that allows uncertainty quantification. We find that there are ≈2–3 impulsive events per minute, with a lifetime of about 10–20 minutes. Moreover, for all the three passbands, the distribution of power-law slope α peaks above 2. Our exploration of correlations among the frequency of impulsive events and their timescales and peak energy suggests that conduction losses dominate over radiative cooling losses. All these findings suggest that impulsive heating is a viable heating mechanism in QS corona.
Highlights
Full disk images of the Sun taken in extreme ultraviolet (EUV) and X-rays, consist of three features: Active Regions (ARs), Coronal holes (CHs), and Quiet Sun (QS)
Note that we show time series of magnetic flux density of the corresponding pixel taken from the Helioseismic and Magnetic Imager (HMI; Scherrer et al 2012) on board Solar Dynamics Observatory (SDO) corresponding to the Atmospheric Imaging Assembly (AIA) Field of View (FOV)
It is important to note that since Pauluhn and Solanki Model (PSM) is a statistical model which generates a representation of the observations “statistically”, one should not perform a point by point comparison of the simulations with the observations
Summary
Full disk images of the Sun taken in extreme ultraviolet (EUV) and X-rays, consist of three features: Active Regions (ARs), Coronal holes (CHs), and Quiet Sun (QS). It is observed that the properties of loops in ARs are well described by the impulsive heating scenario (Ghosh et al 2017; Tripathi et al 2009; Warren et al 2008; Gupta & Tripathi 2015; Winebarger et al 2013b, and references therein) Such a scenario has been studied independently in ARs using a variety to techniques like Time lag analysis (Viall & Klimchuk 2012, 2013, 2015, 2016, 2017), Differential Emission Measure (DEM) and Doppler shifts analysis (see e.g., Tripathi et al 2010, 2011, 2012; Winebarger et al 2011; Warren et al 2012; Winebarger et al 2013a; Subramanian et al 2014; Del Zanna et al 2015), hydrodynamic modelling Since the QS regions have a very diffuse structure, it is not possible to count individual events and understand the energetics of these events in the QS
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