Solar Flares & CMEs and their impact across the Solar System: Observations, Theory, and Machine learning

Topic: Solar Physics / Planets and Exoplanets

Session Title: Solar Flares & CMEs and their impact across the Solar System: Observations, Theory, and Machine learning

Description:

In today’s space-based technological era, the immediate and profound impacts of solar flares and coronal mass ejections (CMEs) on the interplanetary and geospace cannot be understated. Understanding the intricate mechanisms behind these powerful solar phenomena and devising effective prediction methods have become pivotal.

To address this challenge, our research community now engages with a wide spectrum of data, embracing varied physical phenomena, features, and predictive indicators.
Methodologies encompass a diverse range of data sources, from high-resolution solar images for gathering information about solar surface features, active regions, and potential eruption locations. Additionally, solar magnetograms, radio observations for monitoring plasma distributions, coronagraph images, and solar wind monitoring data are employed. This wealth of data also fuels advancements in new data-driven tools (e.g. artificial intelligence-based and mathematical morphology tools), significantly improving our analytical capabilities. Persisting in our investigation of these areas holds the potential for overcoming the current challenges in comprehending the evolution of large solar flares and CMEs, and for strengthening the model’s reliability. This pursuit, where fundamental and operational research meet, delivers encouraging results that could catalyze new mission launches and set the groundwork for more in-depth future studies. These efforts are further greatly enhanced by the advanced data expected from the next generation of state-of-the-art observational and forecasting tools.

Our proposed meeting session aims to comprehensively address crucial flare and CME-related research aspects, specifically focusing on their evolution and prediction. We welcome contributions that cover a wide range of research methodologies, from observational and modeling techniques (including data analysis and numerical approaches) to forecasting methods. This includes the utilization of statistical models, as well as the latest advancements in machine learning and artificial intelligence tools, to enhance our predictive capabilities and validation techniques.

Zenodo Community
In our commitment to open science and reproducibility, contributors will be encouraged to archive presentations and related materials on a dedicated “Machine Learning in the Solar System” Zenodo community.

Session Objectives
– Foster an inclusive learning space where everyone is warmly welcomed, and no question is considered trivial – embracing a culture of shared learning and mutual respect.
– Encourage discussions that explore the diverse applications of machine learning techniques and foster cross-pollination of ideas and collaboration across scientific domains.
– Promote a collaborative environment that underscores the advantages of open science and FAIR data standards, placing a premium on transparency, accessibility, and the shared utilisation of resources for the collective benefit.

Organiser(s): 

Simone Chierichini (University of Sheffield)
Ronish Mugatwala (University of Sheffield)
Dr Marianna Korsos (University of Sheffield)
Dr Paul J. Wright (Dublin Institute for Advanced Studies; DIAS)
Dr Shane Maloney (DIAS)
Dr Alexandra Fogg (DIAS)
Julio Hernandez Camero (University College London, UCL)
Harshita Gandhi (Aberystwyth University)
Dr Sophie Murray (DIAS)
Prof. Peter Gallagher (DIAS)
Prof. Caitríona Jackman (DIAS)
Prof. Lucie Green (UCL)
Prof. Huw Morgan (Aberystwyth University)

Schedule:

Venue: WILB-LT02

Session 1: Friday 19^th July, 09:00 – 11:00

Venue: WILB-LT02

Session 2: Friday 19^th July, 14:00 – 16:00