No abstract

<p>The perplexing mystery of what maintains the solar coronal temperature at about a million K, while the visible disc of the Sun is only at 5800 K, has been a long standing problem in solar physics. A recent study by Mondal et al. (2020, ApJ, 895, L39)  has provided the first evidence for the presence of numerous ubiquitous impulsive emissions at low radio frequencies from the quiet sun regions, which could hold the key to solving this mystery. These Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs) occur at rates of about five hundred events per minute, and their strength is only a few percent of the background steady emission. Based on earlier work with events of larger flux densities and theoretical considerations, WINQSEs are expected to be compact in the image plane. To characterise the spatial structure of WINQSEs, we have developed a pipeline based on an unsupervised machine learning approach. We first identify the boundaries of the radio sun using edge detection techniques, and detect peaks within the solar boundary. Density-Based Spatial Clustering of Application with Noise (DBSCAN), an unsupervised machine learning algorithm, is used to classify the peaks as isolated or clustered. It is also used to find the optimal hyper-parameters for peak-fitting. The peaks are then fit with Gaussian models, and statistical and heuristic filtering criteria are used to obtain robust fits for a subset of these WINQSEs . We find that the vast majority of WINQSEs can be described by well behaved compact Gaussians. By its very design, this approach is focused on morphological characterisation of these weak features and is better suited for identifying them than earlier attempts. We present here our first results of the observed distributions of intensities, sizes and axial ratios of the Gaussian models for WINQSEs arrived at from analysis of multiple independent datasets.</p>

<p>It has been a long standing problem as to how the solar corona can maintain its million K temperature, while the photosphere, which is the lowest layer of the solar atmosphere, is only at a temperature of 5800 K. A very promising theory to explain this is the “nanoflare” hypothesis, which suggests that numerous flares of energies ~10<sup>24</sup> ergs are always happening in the solar corona, and maintain its million K temperature. However, detecting these nanoflares directly is challenging with the current instrumentation as they are hypothesised to occur at very small spatial, temporal and energy scales. These nanoflares are expected to produce nonthermal electrons, which are expected to emit in the radio band. These nonthermal emissions are often brighter than their thermal counterparts and might be detectable with current radio instruments. Due to their importance multiple searches for these nonthermal emissions have been done, but thus far they have been  limited to active regions. The quiet corona is also hot, and often comprises the bulk of the coronal region, so it is equally important to understand the physical processes which maintain this medium at MK temperatures. We describe the results from our effort to use the data from the Murchison Widefield Array (MWA) to search for impulsive radio emissions in the quiet solar corona. By pushing the detection threshold of nonthermal emission by about two orders of magnitude lower than previous studies, we have uncovered ubiquitous very impulsive nonthermal emissions from the quiet sun. We refer to these emissions as Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs). Using independent observations spanning very different solar conditions we show that WINQSEs are present throughout the quiet corona at all times. Their occurrence rate lies in the range of many hundreds to about a thousand per minute, implying that on average order 10 or so WINQSEs are present in every 0.5 s MWA image. Preliminary estimates suggest that WINQSEs have a bandwidth of ~2 MHz. Buoyed by  their possible connection to the hypothesised “nanoflares”, we are pursuing several projects to characterise and understand them. These include developing machine learning algorithms to identify WINQSEs in radio images and characterise their morphologies; exploring the ability of the present generation EUV and X-ray instruments to estimate the energy corresponding to the brightest of WINQSEs; and attempting very high time resolution imaging to explore their temporal structure. In this talk, I will present the results from the past and ongoing projects about WINQSEs and argue that these might be a key step towards detecting “nanoflares” and the resolution of the coronal heating problem.</p><p> </p><p> </p>