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Post-flare arcades are well-known components of solar flare evolution, which have been observed for several decades. Coronal rain, cascades of catastrophically cooled plasma, outlines the loops and provides eye-catching evidence of the recent flare. These events are acknowledged to be common, but the scientific literature does not include any statistical overview documenting just how common the phenomenon actually is. This study reviews Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO AIA) observations of 241 flares collected from the Space Weather Prediction Center database between 2011 and 2018. The flares cover the entire strength range of the C, M, and X GOES classes, and are distributed evenly across the SDO-observed majority of Solar Cycle 24. We find that post-flare arcade rain occurs for nearly all X- and most M-class flares, but that it tapers off rapidly within C-class flares. There appears to be a cut-off point around C5, below which the occurrence of post-flare arcade rain drops significantly. There is also a general positive correlation between GOES class and the average duration of post-flare rain events. Post-flare arcade rain events in X- and M-class flares appear to track with the sunspot number, providing a potential new tool for estimating, if not predicting, solar cycle strength. Furthermore, arcades are observed to persist for up to several days after the originating flare, transitioning from hosting post-flare rain to typical quiescent active region condensations. These results open up further avenues for future research, including new methods to estimate energy deposition and to gain greater insight into steady active region heating.

 

The authors consider a bistatic configuration with a stationary transmitter transmitting unknown waveforms of opportunity and a single moving receiver and present a deep learning (DL) framework for passive synthetic aperture radar (SAR) imaging. They approach DL from an optimisation based perspective and formulate image reconstruction as a machine learning task. By unfolding the iterations of a proximal gradient descent algorithm, they construct a deep recurrent neural network (RNN) that is parameterised by the transmitted waveforms. They cascade the RNN structure with a decoder stage to form a recurrent auto-encoder architecture. They then use backpropagation to learn transmitted waveforms by training the network in an unsupervised manner using SAR measurements. The highly non-convex problem of backpropagation is guided to a feasible solution over the parameter space by initialising the network with the known components of the SAR forward model. Moreover, prior information regarding the waveform structure is incorporated during initialisation and backpropagation. They demonstrate the effectiveness of the DL-based approach through numerical simulations that show focused, high contrast imagery using a single receiver antenna at realistic signal-to-noise-ratio levels. 

In this paper, we discuss the outcomes of the follow-up campaign of SN 2018ijp, discovered as part of the Zwicky Transient Facility survey for optical transients. Its first spectrum shows similarities to broad-lined Type Ic supernovae around maximum light, whereas later spectra display strong signatures of interaction between rapidly expanding ejecta and a dense H-rich circumstellar medium, coinciding with a second peak in the photometric evolution of the transient. This evolution, along with the results of modeling of the first light-curve peak, suggests a scenario where a stripped star exploded within a dense circumstellar medium. The two main phases in the evolution of the transient could be interpreted as a first phase dominated by radioactive decays, and a later interaction-dominated phase where the ejecta collide with a pre-existing shell. We therefore discuss SN 2018jp within the context of a massive star depleted of its outer layers exploding within a dense H-rich circumstellar medium.