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Register a new userDetermining heating time scales in solar active region cores from AIA/SDO Fe XVIII images
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Ignacio Ugarte-Urra
Publication date
2013
fields
Physics
and research's language is
English
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We present a study of the frequency of transient brightenings in the core of solar active regions as observed in the Fe XVIII line component of AIA/SDO 94 A filter images. The Fe XVIII emission is isolated using an empirical correction to remove the contribution of warm emission to this channel. Comparing with simultaneous observations from EIS/Hinode, we find that the variability observed in Fe XVIII is strongly correlated with the emission from lines formed at similar temperatures. We examine the evolution of loops in the cores of active regions at various stages of evolution. Using a newly developed event detection algorithm we characterize the distribution of event frequency, duration, and magnitude in these active regions. These distributions are similar for regions of similar age and show a consistent pattern as the regions age. This suggests that these characteristics are important constraints for models of solar active regions. We find that the typical frequency of the intensity fluctuations is about 1400s for any given line-of-sight, i.e. about 2-3 events per hour. Using the EBTEL 0D hydrodynamic model, however, we show that this only sets a lower limit on the heating frequency along that line-of-sight.
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NuSTAR is a highly sensitive focusing hard X-ray (HXR) telescope and has observed several small microflares in its initial solar pointings. In this paper, we present the first joint observation of a microflare with NuSTAR and Hinode/XRT on 2015 April 29 at ~11:29 UT. This microflare shows heating of material to several million Kelvin, observed in Soft X-rays (SXRs) with Hinode/XRT, and was faintly visible in Extreme Ultraviolet (EUV) with SDO/AIA. For three of the four NuSTAR observations of this region (pre-, decay, and post phases) the spectrum is well fitted by a single thermal model of 3.2-3.5 MK, but the spectrum during the impulsive phase shows additional emission up to 10 MK, emission equivalent to A0.1 GOES class. We recover the differential emission measure (DEM) using SDO/AIA, Hinode/XRT, and NuSTAR, giving unprecedented coverage in temperature. We find the pre-flare DEM peaks at ~3 MK and falls off sharply by 5 MK; but during the microflares impulsive phase the emission above 3 MK is brighter and extends to 10 MK, giving a heating rate of about $2.5 times 10^{25}$ erg s$^{-1}$. As the NuSTAR spectrum is purely thermal we determined upper-limits on the possible non-thermal bremsstrahlung emission. We find that for the accelerated electrons to be the source of the heating requires a power-law spectrum of $delta ge 7$ with a low energy cut-off $E_{c} lesssim 7$ keV. In summary, this first NuSTAR microflare strongly resembles much more powerful flares.
The Interface Region Imaging Spectrograph (IRIS) has observed bright spots at the transition region footpoints associated with heating in the overlying loops, as observed by coronal imagers. Some of these brightenings show significant blueshifts in the Si iv line at 1402.77 A (logT[K] = 4.9). Such blueshifts cannot be reproduced by coronal loop models assuming heating by thermal conduction only, but are consistent with electron beam heating, highlighting for the first time the possible importance of non-thermal electrons in the heating of non-flaring active regions. Here we report on the coronal counterparts of these brightenings observed in the hot channels of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory. We show that the IRIS bright spots are the footpoints of very hot and transient coronal loops which clearly experience strong magnetic interactions and rearrangements, thus confirming the impulsive nature of the heating and providing important constraints for their physical interpretation.
We present an empirical model based on the visible area covered by coronal holes close to the central meridian in order to predict the solar wind speed at 1 AU with a lead time up to four days in advance with a 1hr time resolution. Linear prediction functions are used to relate coronal hole areas to solar wind speed. The function parameters are automatically adapted by using the information from the previous 3 Carrington Rotations. Thus the algorithm automatically reacts on the changes of the solar wind speed during different phases of the solar cycle. The adaptive algorithm has been applied to and tested on SDO/AIA-193A observations and ACE measurements during the years 2011-2013, covering 41 Carrington Rotations. The solar wind speed arrival time is delayed and needs on average 4.02 +/- 0.5 days to reach Earth. The algorithm produces good predictions for the 156 solar wind high speed streams peak amplitudes with correlation coefficients of cc~0.60. For 80% of the peaks, the predicted arrival matches within a time window of 0.5 days of the ACE in situ measurements. The same algorithm, using linear predictions, was also applied to predict the magnetic field strength from coronal hole areas but did not give reliable predictions (cc~0.2).
Using data from the Extreme-ultraviolet Imaging Spectrometer aboard Hinode, we have studied the coronal plasma in the core of two active regions. Concentrating on the area between opposite polarity moss, we found emission measure distributions having an approximate power-law form EM$propto T^{2.4}$ from $log,T = 5.5$ up to a peak at $log,T = 6.55$. We show that the observations compare very favorably with a simple model of nanoflare-heated loop strands. They also appear to be consistent with more sophisticated nanoflare models. However, in the absence of additional constraints, steady heating is also a viable explanation.
Constraining the frequency of energy deposition in magnetically-closed active region cores requires sophisticated hydrodynamic simulations of the coronal plasma and detailed forward modeling of the optically-thin line-of-sight integrated emission. However, understanding which set of model inputs best matches a set of observations is complicated by the need for any proposed heating model to simultaneously satisfy multiple observable constraints. In this paper, we train a random forest classification model on a set of forward-modeled observable quantities, namely the emission measure slope, the peak temperature of the emission measure distribution, and the time lag and maximum cross-correlation between multiple pairs of AIA channels. We then use our trained model to classify the heating frequency in every pixel of active region NOAA 1158 using the observed emission measure slopes, peak temperatures, time lags, and maximum cross-correlations and are able to map the heating frequency across the entire active region. We find that high-frequency heating dominates in the inner core of the active region while intermediate frequency dominates closer to the periphery of the active region. Additionally, we assess the importance of each observed quantity in our trained classification model and find that the emission measure slope is the dominant feature in deciding with which heating frequency a given pixel is most consistent. The technique presented here offers a very promising and widely applicable method for assessing observations in terms of detailed forward models given an arbitrary number of observable constraints.
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