June 11, 12:00 pm - 1:00 pm

June 11, 12:00 pm - 1:00 pm

Determining non-thermal velocity distributions from spatially-averaged EUV spectra observed with SERTS and Hinode/EIS

Aaron Coyner (Catholic University of America and NASA/GSFC)

Unresolved non-thermal broadenings have been reported in observations of solar EUV spectra for a number of studies covering a wide array of instruments. These broadenings are generally attributed to additional unresolved motions beyond the typical thermal broadening within the radiating coronal plasma loops. In addition, these motions have been suggested as an additional energy source for possible coronal heating. Observationally constraining the unresolved velocities is necessary to provide a limitation on the maximum available energy in the coronal plasma to facilitate coronal heating. For this study we take a statistical distribution approach to determine an expected non-thermal velocity component of observed EUV line profiles to provide an observational limitation on broadenings for all coronal heating models to address. We have analyzed spatially-averaged spectra from both the Solar EUV Research Telescope and Spectrograph (SERTS) over the EUV bandpass from 170-420 angstroms in a variety of solar conditions, from quiet sun to active region to off-limb data. For each instrument, we construct a non-thermal velocity, <V_nth²>, distributions for active region and quiet sun emission lines respectively. We then fit each distribution with a Gaussian to determine the typical unresolved velocities observed in both quiet sun and active region distributions. Ideally, if no non-thermal component exists the distributions would all peak at zero, but in the case of the SERTS observations, we find the distributions peak at velocities between 21-25 km/s regardless of solar activity. Building off of the SERTS analysis, we have analyzed a sample of active region spectra observed with Hinode/EIS and present here a comparison of the observed velocity distributions for both the SERTS and EIS spectral data.