34 research outputs found
Exploring the potential of neural networks to predict statistics of solar wind turbulence
Time series datasets often have missing or corrupted entries, which need to
be ignored in subsequent data analysis. For example, in the context of space
physics, calibration issues, satellite telemetry issues, and unexpected events
can make parts of a time series unusable. Various approaches exist to tackle
this problem, including mean/median imputation, linear interpolation, and
autoregressive modeling. Here we study the utility of artificial neural
networks (ANNs) to predict statistics, particularly second-order structure
functions, of turbulent time series concerning the solar wind. Using a dataset
with artificial gaps, a neural network is trained to predict second-order
structure functions and then tested on an unseen dataset to quantify its
performance. A small feedforward ANN, with only 20 hidden neurons, can predict
the large-scale fluctuation amplitudes better than mean imputation or linear
interpolation when the percentage of missing data is high. Although, they
perform worse than the other methods when it comes to capturing both the shape
and fluctuation amplitude together, their performance is better in a
statistical sense for large fractions of missing data. Caveats regarding their
utility, the optimisation procedure, and potential future improvements are
discussed.Comment: 17 pages, 5 figures, 2 table
Statistics of Turbulence in the Solar Wind. I. What is the Reynolds Number of the Solar Wind?
The Reynolds number, Re, is an important quantity for describing a turbulent
flow. It tells us about the bandwidth over which energy can cascade from large
scales to smaller ones, prior to the onset of dissipation. However, calculating
it for nearly collisionless plasmas like the solar wind is challenging.
Previous studies have used "effective" Reynolds number formulations, expressing
Re as a function of the correlation scale and either the Taylor scale or a
proxy for the dissipation scale. We find that the Taylor scale definition of
the Reynolds number has a sizeable prefactor of approximately 27, which has not
been employed in previous works. Drawing from 18 years of data from the Wind
spacecraft at 1 au, we calculate the magnetic Taylor scale directly and use
both the ion inertial length and the magnetic spectrum break scale as
approximations for the dissipation scale, yielding three distinct Re estimates
for each 12-hour interval. Average values of Re range between 116,000 and
3,406,000, within the general distribution of past work. We also find
considerable disagreement between the methods, with linear associations of
between 0.38 and 0.72. Although the Taylor scale method is arguably more
physically motivated, due to its dependence on the energy cascade rate, more
theoretical work is needed in order to identify the most appropriate way of
calculating effective Reynolds numbers for kinetic plasmas. As a summary of our
observational analysis, we make available a data product of 28 years of 1 au
solar wind and magnetospheric plasma measurements from Wind