Abstract Details
| Name: Rohit Affiliation: ncra-tifr Conference ID: ASI2016_649 Title : Characterisation of non-thermal solar emissions at low radio frequencies. Authors and Co-Authors : Rohit Sharma NCRA-TIFR, Pune Divya Oberoi NCRA-TIFR, Pune Abstract Type : Oral Abstract Category : Sun and the Solar System Abstract : Murchison Widefield Array (MWA) located in Western Australia is an excellent instrument for spectroscopic solar imaging at meterwavelengths (80-300 MHz). The MWA solar dynamic spectra show weak features, typically spanning order a MHz and lasting a few seconds. These features are seen even during periods of low solar activity and their short temporal and spectral spans imply a non-thermal origin. Their characteristics are unlike those of any of the usual known types of solar bursts, but come closest to being heavily scaled down versions of type III bursts. In 1963 Parker hypothesised the presence of nanoflares as a solution to the coronal heating problem. These nanoflares are small flare events arising from magnetic reconnection, and a large number of them are required to routinely take place in the twisted and tangled coronal magnetic field carpet. A statistically steady background arising from a large number of such weak flares is meant to provide the missing coronal heat flux. No direct observations of nanoflares have been made yet . We explore the possibility that the weak non-thermal emissions observed in MWA data are radio signatures of nanoflares. As a step towards this, we have developed a technique to quantify the frequency of occurrence of such emission and its strength. This technique takes as an input flux calibrated dynamic spectrum for a carefully chosen MWA baseline, and decomposes it into a thermal and non-thermal parts. We model the observed emission as comprising of a thermal and non-thermal components. For calibrated data, over short spectral and temporal spans, the thermal emission will follow a Gaussian distribution while the non-thermal emissions will fall outside this Gaussian. In order to quantitatively separate these two we use the Gaussian mixtures technique. This technique models the data as a sum of many Gaussians and associated a probability with each data point of it being a part of each of the Gaussians in the model. We present our results from applying this technique to an hour of solar data from 3 September, 2013 (03:40-04:40 UT). |