Machine Learning-Based Characterization of Solar p-Mode Frequency Shifts during Solar Cycle 25

TL;DR

This paper applies machine learning and time-series analysis to characterize solar p-mode frequency shifts during Solar Cycle 25, aiming to improve understanding of solar interior dynamics and space weather forecasting.

Contribution

It develops and applies machine learning methods to analyze p-mode frequency shifts, linking solar interior processes with surface activity and space weather indicators.

Findings

Identified patterns in p-mode frequency shifts during Solar Cycle 25.

Demonstrated the potential of machine learning to forecast solar interior dynamics.

Enhanced understanding of the connection between solar interior and surface activity.

Abstract

The solar interior is probed by the properties of the Sun's acoustic oscillations (p-modes) observed on the solar surface. The frequencies of these p-modes measured in the last three decades show long term variation similar to the 11 year cyclic behaviour exhibited by 10.7 cm radio flux, sunspot numbers and other solar activity indices. It is also now established that the cyclic behavior of some of the solar proxies are connected with geomagnetic activities and have implications for space weather. Hence, in recent years efforts have been made using machine-learning methods to forecast these solar proxies with a view to improve our understanding of space weather. Developing a comparable method for forecasting p-mode frequency shifts is therefore of interest for two reasons. Firstly, it will facilitate future investigations into its potential role in tracing energy drivers from the Sun's interior to the geospace response by improving models of solar interior dynamics to coronal and heliospheric plasma conditions. In other words, it will help establish a more robust and quantitative link between the Sun's interior and its exterior. Secondly, it may provide us with an independent indicator or an early indicator of ascending and descending phase of solar activity which might be useful for space weather forecasting. In this article, we develop and apply the standard time-series analysis and machine-learning based methods to characterise p-mode frequency shifts for the remaining solar cycle 25.