  The solar corona is extremely dynamic. Every leap in observational
capabilities has been accompanied by unexpected revelations of complex dynamic
processes. The ever more sensitive instruments now allow us to probe events
with increasingly weaker energetics. A recent leap in the low-frequency radio
solar imaging ability has led to the discovery of a new class of emissions,
namely Weak Impulsive Narrowband Quiet Sun Emissions
\citep[WINQSEs;][]{mondal2020}. They are hypothesized to be the radio
signatures of coronal nanoflares and could potentially have a bearing on the
long standing coronal heating problem. In view of the significance of this
discovery, this work has been followed up by multiple independent studies.
These include detecting WINQSEs in multiple datasets, using independent
detection techniques and software pipelines, and looking for their counterparts
at other wavelengths. This work focuses on investigating morphological
properties of WINQSEs and also improves upon the methodology used for detecting
WINQSEs in earlier works. We present a machine learning based algorithm to
detect WINQSEs, classify them based on their morphology and model the isolated
ones using 2D Gaussians. We subject multiple datasets to this algorithm to test
its veracity. Interestingly, despite the expectations of their arising from
intrinsically compact sources, WINQSEs tend to be resolved in our observations.
We propose that this angular broadening arises due to coronal scattering.
WINQSEs can, hence, provide ubiquitous and ever-present diagnostic of coronal
scattering (and, in turn, coronal turbulence) in the quiet sun regions, which
has not been possible till date.
