Reconstruction of electron precipitation spectra at the top of the upper atmosphere using 427.8 nm auroral images

Abstract

We present an innovative method to reconstruct the characteristics of precipitated electrons in auroral regions from optical measurements. This method is based on an optimization implemented between numerical simulations of the Transsolo code and tomographic maps made from the Auroral Large Imaging System (ALIS) network. We focus on the Volume Emission Rate (VER) of the blue line N 2 + $ {\mathrm{N}}_2^{+}$ 1NG 427.8 nm, which is the most representative line of the energy deposition by electrons. The optimization is tested with the ALIS measurements carried out on March 05, 2008, at 18:41:30 UT and 18:42:40 UT. The reconstruction is performed by extracting the energy flux and the mean energy of the precipitating particles. Both Maxwellian and quasi-monoenergetic energy distributions are considered. Calculations performed with a Maxwellian energy distribution yielded a mean energy ranging from 1.8 to 5.2 keV with energy flux from 0.1 to 44.3 erg·cm−2·s−1 for 18:41:30 UT, and a mean energy from 2.2 to 9.5 keV with energy flux from 2.1 to 136.7 erg·cm−2·s−1 for 18:42:40 UT. Assuming a quasi-monoenergetic energy distribution, we find a mean energy ranging from 4.2 to 11.8 keV with energy flux ranging from 0.1 to 45 erg·cm−2·s−1 for 18:41:30 UT, and 8 to 17.1 keV with energy flux ranging from 2.2 to 110.1 erg·cm−2·s−1 for 18:42:40 UT. Moreover, we show this method allows us to reconstruct the energy characteristic of the precipitating electrons on a large region covering approximately 150 km × 150 km. This study also shows that some VER profiles of the maps are better fitted by quasi mono-energetic distributions while some others correspond to broadband distributions. It appears clearly that the energy flux is linked to the column integrated intensity, the mean energy is linked with the peak altitude of the emission, and the width of the energy distribution with the altitude thickness of the emissions.

Keywords

• space weather
• aurora
• optical data
• optimization
• physical model
• precipitated electrons