Extraction and Analysis of Coronal High-temperature Components Based on Outlier Detection

The extraction of high-temperature regions in active regions(ARs)is an important means to help understand the mechanism of coronal heating.The important observational means of high-temperature radiation in ARs is the main emission line of Fe XVⅢin the 94?of the Atmospheric Imaging Assembly.However,the diagnostic algorithms for Fe XVⅢ,including the differential emission measure(DEM)and linear diagnostics proposed by Del based on the DEM,have been greatly limited for a long time,and the results obtained are different from the predictions.In this paper,we use the outlier detection method to establish the nonlinear correlation between 94?and 171,193,211?based on the former researches by others.A neural network based on 171,193,211?is constructed to replace the low-temperature emission lines in the ARs of 94?.The predicted results are regarded as the low-temperature components of 94?,and then the predicted results are subtracted from 94?to obtain the outlier component of 94?,or Fe XVⅢ.Then,the outlier components obtained by neural network are compared with the Fe XVⅢobtained by DEM and Del's method,and a high similarity is found,which proves the reliability of neural network to obtain the high-temperature components of ARs,but there are still many differences.In order to analyze the differences between the Fe XVⅢobtained by the three methods,we subtract the Fe XVⅢobtained by the DEM and Del's method from the Fe XVⅢobtained by the neural network to obtain the residual value,and compare it with the results of Fe XIV in the temperature range of 6.1-6.45 MK.It is found that there is a great similarity,which also shows that the Fe XVⅢobtained by DEM and Del's method still has a large low-temperature component dominated by Fe XIV,and the Fe XVⅢobtained by neural network is relatively pure.

The surge-like eruption of a miniature filament

We report on the rare eruption of a miniature Hα filament that took the form of a surge. The filament first underwent a full development within 46 min and then began to erupt 9 min later, followed by a compact, impulsive X-ray class M2.2 flare with a two-ribbon nature only at the early eruption phase. During the eruption, its top rose, whereas the two legs remained rooted in the chromosphere and showed little swelling perpendicular to the rising direction. This led to a surge-like eruption with a narrow angular extent. Similar to the recent observations for standard and blowout X- ray jets by Moore et al., we thus define it as a ＂blowout Hα surge.＂ Furthermore, our observations showed that the eruption was associated with （1） a coronal mass ejection guided by a pre-existing streamer, （2） abrupt, significant, and persistent changes in the photospheric magnetic field around the filament, and （3） a sudden disappearance of a small pore. These observations thus provide evidence that a blowout surge is a small- scale version of a large-scale filament eruption in many aspects. Our observations further suggest that at least part of the Hα surges belong to blowout-type cases, and the exact distinction between the standard and blowout Hα surges is important in understanding their different origins and associated eruptive phenomena.

Predicting the evolution of photospheric magnetic field in solar active regions using deep learning

The continuous observation of the magnetic field by the Solar Dynamics Observatory(SDO)/Helioseismic and Magnetic Imager(HMI)produces numerous image sequences in time and space.These sequences provide data support for predicting the evolution of photosphericmagnetic field.Based on the spatiotemporal long short-term memory(LSTM)network,we use the preprocessed data of photospheric magnetic field in active regions to build a prediction model for magnetic field evolution.Because of the elaborate learning and memory mechanism,the trained model can characterize the inherent relationships contained in spatiotemporal features.The testing results of the prediction model indicate that(1)the prediction pattern learned by the model can be applied to predict the evolution of new magnetic field in the next 6 hours that have not been trained,and predicted results are roughly consistent with real observed magnetic field evolution in terms of large-scale structure and movement speed;(2)the performance of the model is related to the prediction time;the shorter the prediction time,the higher the accuracy of the predicted results;(3)the performance of themodel is stable not only for active regions in the north and south but also for data in positive and negative regions.Detailed experimental results and discussions on magnetic flux emergence and magnetic neutral lines finally show that the proposed model could effectively predict the large-scale and short-term evolution of the photospheric magnetic field in active regions.Moreover,our study may provide a reference for the spatiotemporal prediction of other solar activities.

Coupling of small-and large-scale filament eruptions

We present observations of the eruption of a large-scale quiescent filament （LF） that is associated with the formation and eruption of a miniature filament （MF）. As a result of convergence and subsequent cancelation of opposite-polarity magnetic flux, MF was formed just below the spine of the LF＇s right seg- ment. Probably triggered by a nearby newly emerging flux, MF underwent a failed eruption immediately after its full development, which first ejected away from the spine of LF and then drained back to the Sun. This eruption no sooner started than the overlying LF＇s right segment began to rise slowly and the LF＇s other parts were also disturbed, and eventually the whole LF erupted bodily and quickly. These observa- tions suggest that the MF can serve as an intermediary that links the photospheric small-scale magnetic-field activities to the eruption of the overlying large filament. It appears that, rather than directly interacting with the supporting magnetic field of LF, small-scale flux cancelation and emergence in the LF＇s channel can manifest themselves as the formation and eruption of MF and so indirectly affect the stability of LE

Twist in a polar blowout jet

It is well known that some coronal jets exhibit helical structures and un- twisting. We attempt to inspect the origin of twist in a blowout jet. By means of multiwavelength and multi-angle observations from Solar Dynamics Observatory （SDO） and Solar Terrestrial Relations Observatory-Ahead （STEREO-A）, we firstly report a polar untwisting jet that is a blowout jet which leads to a jet-like coronal mass ejection. From the viewpoint of SDO, the jet shows clear untwisting behavior and two jet-spires. However, from the viewpoint of STEREO-A the jet actually comes from the whiplike prominence eruption and is followed by a white-light jet. Our observations indicate that twist in blowout jets may result from the erupting mini-prominences/mini- filaments in the jet base.