English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Context: The analysis of the thermal part of velocity distribution functions(VDF) is fundamentally important for understanding the kinetic physics thatgoverns the evolution and dynamics of space plasmas. However, calculating theproton core, beam and alpha-particle parameters for large data sets of VDFs isa time consuming and computationally demanding process that always requiressupervision by a human expert.  Aims: We developed a machine learning tool that can extract proton core, beamand alpha-particle parameters using images (2-D grid consisting pixel values)of VDFs.  Methods: A database of synthetic VDFs is generated, which is used to train aconvolutional neural network that infers bulk speed, thermal speed and densityfor all three particle populations. We generate a separate test data set ofsynthetic VDFs that we use to compare and quantify the predictive power of theneural network and a fitting algorithm.  Results: The neural network achieves significantly smaller root-mean-squareerrors to infer proton core, beam and alpha-particle parameters than atraditional fitting algorithm.  Conclusion: The developed machine learning tool has the potential torevolutionize the processing of particle measurements since it allows thecomputation of more accurate particle parameters than previously used fittingprocedures.

astronomy and astrophysics

A powerful machine learning technique to extract proton core, beam and  alpha-particle parameters from velocity distribution functions in space  plasmas