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Standing slow-mode waves have been recently observed in flaring loops by the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO). By means of the coronal seismology technique transport coefficients in hot ($\sim$10 MK) plasma were determined by Wang et al.(2015, Paper I), revealing that thermal conductivity is nearly suppressed and compressive viscosity is enhanced by more than an order of magnitude. In this study we use 1D nonlinear MHD simulations to validate the predicted results from the linear theory and investigate the standing slow-mode wave excitation mechanism. We first explore the wave trigger based on the magnetic field extrapolation and flare emission features. Using a flow pulse driven at one footpoint we simulate the wave excitation in two types of loop models: model 1 with the classical transport coefficients and model 2 with the seismology-determined transport coefficients. We find that model 2 can form the standing wave pattern (within about one period) from initial propagating disturbances much faster than model 1, in better agreement with the observations. Simulations of the harmonic waves and the Fourier decomposition analysis show that the scaling law between damping time ($\tau$) and wave period ($P$) follows $\tau\propto{P^2}$ in model 2, while $\tau\propto{P}$ in model 1. This indicates that the largely enhanced viscosity efficiently increases the dissipation of higher harmonic components, favoring the quick formation of the fundamental standing mode. Our study suggests that observational constraints on the transport coefficients are important in understanding both, the wave excitation and damping mechanisms., Comment: 19 pages, 17 figures,2 tables, 1 animation; accepted by ApJ

It is widely believed that loops observed in the solar atmosphere trace out magnetic field lines. However, the degree to which magnetic field extrapolations yield field lines that actually do follow loops has yet to be studied systematically. In this paper we apply three different extrapolation techniques - a simple potential model, a NLFF model based on photospheric vector data, and a NLFF model based on forward fitting magnetic sources with vertical currents - to 15 active regions that span a wide range of magnetic conditions. We use a distance metric to assess how well each of these models is able to match field lines to the 12,202 loops traced in coronal images. These distances are typically 1-2". We also compute the misalignment angle between each traced loop and the local magnetic field vector, and find values of 5-12$^\circ$. We find that the NLFF models generally outperform the potential extrapolation on these metrics, although the differences between the different extrapolations are relatively small. The methodology that we employ for this study suggests a number of ways that both the extrapolations and loop identification can be improved., Comment: Accepted for publication in ApJ

We investigate two sympathetic filament eruptions observed by the New Vacuum Solar Telescope (NVST) on 2015 October 15. The full picture of the eruptions is obtained from the corresponding SDO/AIA observations. The two filaments start from active region NOAA 12434 in the north and end in one large quiescent filament channel in the south. The left filament erupts firstly, followed by the right filament eruption about 10 minutes later. Clear twist structure and rotating motion are observed in both filaments during the eruption. Both eruptions are failed, since the filaments firstly rise up, then flow towards the south and merge into the southern large quiescent filament. We also observe repeating activations of mini filaments below the right filament after its eruption. Using magnetic field models constructed based on SDO/HMI magnetograms by flux rope insertion method, we find that the left filament eruption is likely to be triggered by kink instability, while weakening of overlying magnetic fields due to magnetic reconnection at an X-point between the two filament systems might play an important role in the onset of the right filament eruption.

This document provides some technical notes on the polar field correction scheme for the HMI synoptic maps and daily updated synchronic frames. It is intended as a reference for the new data products and for some minor updates on our previous scheme for MDI (Sun et al. 2011)., Comment: Tech notes for HMI synoptic data product

The flux emergence rate of AR 12673 is greater than any values reported in the literature of which we are aware., Comment: Accepted to the Research Notes of the AAS

The physical conditions that determine whether or not solar active regions (ARs) produce strong flares and coronal mass ejections (CMEs) are not yet well understood. Here we investigate the association between electric-current neutralization, magnetic shear along polarity inversion lines (PILs), and eruptive activity in four ARs; two emerging and two well-developed ones. We find that the CME-producing ARs are characterized by a strongly non-neutralized total current, while the total current in the ARs that did not produce CMEs is almost perfectly neutralized. The difference in the PIL-shear between these two groups is much less pronounced, which suggests that the degree of current-neutralization may serve as a better proxy for assessing the ability of ARs to produce CMEs., Comment: ApJL in press

We present a database of 3137 solar flare ribbon events corresponding to every flare of GOES class C1.0 and greater within 45 degrees from the central meridian, from April 2010 until April 2016, observed by the \emph{Solar Dynamics Observatory}. For every event in the database, we compare the GOES peak X-ray flux with corresponding active-region and flare-ribbon properties. We find that while the peak X-ray flux is not correlated with the active region unsigned magnetic flux, it is strongly correlated with the flare ribbon reconnection flux, flare ribbon area, and the fraction of active region flux that undergoes reconnection. We find the relationship between the peak X-ray flux and the flare ribbon reconnection flux to be $I_\mathrm{X,peak} \propto \Phi_\mathrm{ribbon}^{1.5}$. This scaling law is consistent with earlier hydrodynamic simulations of impulsively heated flare loops. Using the flare reconnection flux as a proxy for the total released flare energy $E$, we find that the occurrence frequency of flare energies follows a power-law dependence: $dN/dE \propto E^{-1.6}$ for $10^{31}

The solar active region photospheric magnetic field evolves rapidly during major eruptive events, suggesting appreciable feedback from the corona. Previous studies of these "magnetic imprints" are mostly based on line-of-sight only or lower-cadence vector observations; a temporally resolved depiction of the vector field evolution is hitherto lacking. Here, we introduce the high-cadence (90~s or 135~s) vector magnetogram dataset from the Helioseismic and Magnetic Imager (HMI) that is well suited for investigating the phenomenon. These observations allow quantitative characterization of the permanent, step-like changes that are most pronounced in the horizontal field component ($B_h$). A highly structured pattern emerges from analysis of an archetypical event, \texttt{SOL2011-02-15T01:56}, where $B_h$ near the main polarity inversion line increases significantly during the earlier phase of the associated flare with a time scale of several minutes, while $B_h$ in the periphery decreases at later times with smaller magnitudes and a slightly longer time scale. The dataset also allows effective identification of the "magnetic transient" artifact, where enhanced flare emission alters the Stokes profiles and the inferred magnetic field becomes unreliable. Our results provide insights on the momentum processes in solar eruptions. The dataset may also be useful to the study of sunquakes and data-driven modeling of the corona., Comment: In press in ApJ; Minor corrections; Reference for HMI high-cadence vector magnetograms

In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation. In particular, we improve the state-of-the-art faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pretraining, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance, making it the best model in terms of ROC curves among all the published methods on the FDDB benchmark.