arXiv subjects

J. Bortnik

Publications and source records attributed to J. Bortnik.

3 recordsLinked to original sources

arXiv2024

Nonlinear resonant interactions of radiation belt electrons with intense whistler-mode waves

The dynamics of the Earth's outer radiation belt, filled by energetic electron fluxes, is largely controlled by electron resonant interactions with electromagnetic whistler-mode waves. The most coherent and intense waves resonantly interact with electrons nonlinearly, and the observable effects of such nonlinear interactions cannot be described within the frame of classical quasi-linear models. This paper provides an overview of the current stage of the theory of nonlinear resonant interactions and discusses different possible approaches for incorporating these nonlinear interactions into global radiation belt simulations. We focused on observational properties of whistler-mode waves and theoretical aspects of electron nonlinear resonant interactions between such waves and energetic electrons.

physics.space-ph↗

arXiv2023

Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values

In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft. IBI is a correlation ($R^2$) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day-to-day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014-22 at a resolution of 1sec, and transform it from a time-series into a 6-dimensional space with a corresponding EPB $R^2$ (0-1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model performs best post-sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods. Shapley values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset.

physics.space-ph↗

arXiv2022

Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

astro-ph.IM↗