Automatic Determination of Magnetosonic/Whistler Events in the Solar Wind
Abstract
23 years of observations by the WIND spacecraft has yielded a rich array of high-resolution magnetic field data, where a large fraction displays small-scale structures. In particular, the solar wind is full of magnetosonic-whistler-like fluctuations and kinetic Alfven fluctuations that appear in both the field magnitude and its components. Much of the previous work in the turbulence community has focused on kinetic Alfven waves (KAWs) and the properties of the associated spectral power law, with a break which occurs in the 0.1-5.0 Hz range. This frequency range corresponds to the spacecraft frame frequency of magnetosonic-whistlers (MWs). Because solar wind heating models depend on the sources of dissipation, the ability to consistently distinguish between KAWs and MWs is vital to verifying these models. Given the breadth of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed in the magnetic field data. To this end, a subset of current WIND data will be labeled and used as a training set for a machine learning algorithm aimed at classifying small-scale structures. This algorithm can then be used to catalog the entire WIND magnetic field dataset.
- Publication:
-
Solar Heliospheric and INterplanetary Environment (SHINE 2019)
- Pub Date:
- May 2019
- Bibcode:
- 2019shin.confE.165F