We propose a novel methodology to identity flows in the solar atmosphere and
classify their velocities as either supersonic, subsonic, or sonic. The
proposed methodology consists of three parts. First, an algorithm is applied to
the Solar Dynamics Observatory (SDO) image data to locate and track flows,
resulting in the trajectory of each flow over time. Thereafter, the
differential emission measure inversion method is applied to six AIA channels
along the trajectory of each flow in order to estimate its background
temperature and sound speed. Finally, we classify each flow as supersonic,
subsonic, or sonic by performing simultaneous hypothesis tests on whether the
velocity bounds of the flow are larger, smaller, or equal to the background
sound speed. The proposed methodology was applied to the SDO image data from
the 171 {\AA} spectral line for the date 6 March 2012 from 12:22:00 to 12:35:00
and again for the date 9 March 2012 from 03:00:00 to 03:24:00. Eighteen plasma
flows were detected, 11 of which were classified as supersonic, 3 as subsonic,
and 3 as sonic at a 70% level of significance. Out of all these cases, 2
flows cannot be strictly ascribed to one of the respective categories as they
change from the subsonic state to supersonic and vice versa. We labelled them
as a subclass of transonic flows. The proposed methodology provides an
automatic and scalable solution to identify small-scale flows and to classify
their velocities as either supersonic, subsonic, or sonic. We identified and
classified small-scale flow patterns in flaring loops. The results show that
the flows can be classified into four classes: sub-, super-, trans-sonic, and
sonic. The detected flows from AIA images can be analyzed in combination with
the other high-resolution observational data, such as Hi-C 2.1 data, and be
used for the development of theories of the formation of flow patterns.Comment: 13 pages, 7 figures, Accepted for publication in A&