Multifrequency microwave imaging of weak transients from the quiet solar corona

Understanding the dynamics of the quiet solar corona is important for answering key questions including the coronal heating problem. Multiple studies have suggested small-scale magnetic reconnection events may play a crucial role. These reconnection events are expected to involve acceleration of electrons to suprathermal energies, which can then produce nonthermal observational signatures. However, due to the paucity of sensitive high-fidelity observations capable of probing these nonthermal signatures, most studies were unable to quantify their nonthermal nature. Here we use joint radio observations from the Very Large Array (VLA) and the Expanded Owens Valley Solar Array (EOVSA) to detect transient emissions from the quiet solar corona in the microwave (GHz) domain. While similar transients have been reported in the past, their nonthermal nature could not be adequately quantified due to the unavailability of broadband observations. Using a much larger bandwidth available now with the VLA and EOVSA, in this study, we are able to quantify the nonthermal energy associated with two of these transients. We find that the total nonthermal energy associated with some of these transients can be comparable to or even larger than the total thermal energy of a nanoflare, which underpins the importance of nonthermal energy in the total coronal energy budget.Comment: Accepted for publication in the Ap

Fine structures of solar radio type III bursts and their possible relationship with coronal density turbulence

Solar radio type III bursts are believed to be the most sensitive signatures of near-relativistic electron beam propagation in the corona. A solar radio type IIIb-III pair burst with fine frequency structures, observed by the Low Frequency Array (LOFAR) with high temporal (~10 ms) and spectral (12.5 kHz) resolutions at 30–80 MHz, is presented. The observations show that the type III burst consists of many striae, which have a frequency scale of about 0.1 MHz in both the fundamental (plasma) and the harmonic (double plasma) emission. We investigate the effects of background density fluctuations based on the observation of striae structure to estimate the density perturbation in the solar corona. It is found that the spectral index of the density fluctuation spectrum is about −1.7, and the characteristic spatial scale of the density perturbation is around 700 km. This spectral index is very close to a Kolmogorov turbulence spectral index of −5/3, consistent with a turbulent cascade. This fact indicates that the coronal turbulence may play the important role of modulating the time structures of solar radio type III bursts, and the fine structure of radio type III bursts could provide a useful and unique tool to diagnose the turbulence in the solar corona

A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information

Time series forecasting represents a significant and challenging task across various fields. Recently, methods based on mode decomposition have dominated the forecasting of complex time series because of the advantages of capturing local characteristics and extracting intrinsic modes from data. Unfortunately, most models fail to capture the implied volatilities that contain significant information. To enhance the prediction of contemporary diverse and complex time series, we propose a novel time series forecasting paradigm that integrates decomposition with the capability to capture the underlying fluctuation information of the series. In our methodology, we implement the Variational Mode Decomposition algorithm to decompose the time series into K distinct sub-modes. Following this decomposition, we apply the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to extract the volatility information in these sub-modes. Subsequently, both the numerical data and the volatility information for each sub-mode are harnessed to train a neural network. This network is adept at predicting the information of the sub-modes, and we aggregate the predictions of all sub-modes to generate the final output. By integrating econometric and artificial intelligence methods, and taking into account both the numerical and volatility information of the time series, our proposed framework demonstrates superior performance in time series forecasting, as evidenced by the significant decrease in MSE, RMSE, and MAPE in our comparative experimental results

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

On the source position and duration of a solar type III radio burst observed by LOFAR

The flux of solar type III radio bursts have a time profile of rising and decay phases at a given frequency, which has been actively studied since the 1970s. Several factors that may influence the duration of a type III radio burst have been proposed. In this work, to study the dominant cause of the duration, we investigate the source positions of the front edge, the peak, and the tail edge in the dynamic spectrum of a single and clear type III radio burst. The duration of this type III burst at a given frequency is about 3 s for decameter wave. The beam-formed observations by the LOw-Frequency ARray are used, which can provide the radio source positions and the dynamic spectra at the same time. We find that, for this burst, the source positions of the front edge, the peak, and the tail edge split with each other spatially. The radial speed of the electrons exciting the front edge, the peak, and the tail edge is 0.42c, 0.25c, and 0.16c, respectively. We estimate the influences of the corona density fluctuation and the electron velocity dispersion on the duration, and the scattering effect by comparison with a few short-duration bursts from the same region. The analysis yields that, in the frequency range of 30–41 MHz, the electron velocity dispersion is the dominant factor that determines the time duration of type III radio bursts with long duration, while scattering may play an important role in the duration of short bursts

Effect of a sausage oscillation on radio zebra pattern structures in a solar flare

Sausage modes that are axisymmetric fast magnetoacoustic oscillations of solar coronal loops are characterized by variation of the plasma density and magnetic field, and hence cause time variations of the electron plasma frequency and cyclotron frequency. The latter parameters determine the condition for the double plasma resonance (DPR), which is responsible for the appearance of zebra-pattern (ZP) structures in time spectra of solar type IV radio bursts. We perform numerical simulations of standing and propagating sausage oscillations in a coronal loop modeled as a straight, field-aligned plasma slab, and determine the time variation of the DPR layer locations. Instant values of the plasma density and magnetic field at the DPR layers allowed us to construct skeletons of the time variation of ZP stripes in radio spectra. In the presence of a sausage oscillation, the ZP structures are shown to have characteristic wiggles with the time period prescribed by the sausage oscillation. Standing and propagating sausage oscillations are found to have different signatures in ZP patterns. We conclude that ZP wiggles can be used for the detection of short-period sausage oscillations and the exploitation of their seismological potential