Machine Learning Across the Solar System: The Sun, Planets and Beyond

Description: Machine learning is growing to be an established, powerful tool for the analysis of solar system data. This interdisciplinary session invites both scientific researchers and machine learning practitioners to contribute presentations on how advanced algorithms and machine learning are used to probe various solar system datasets. The session will welcome topics from the Sun to the terrestrial and Jovian planets, and we encourage contributions that showcase curated datasets, cutting-edge methodologies in statistics and machine learning, and those that draw attention to the cooperative relationship between scientific investigation and machine learning techniques.

By bringing together expertise from fields bridging across several discipline boundaries, this session aims to encourage interdisciplinary collaboration between scientific fields. Embracing the principles of open science, we welcome contributions to champion transparent, accessible, and collaborative research practices while adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. We especially welcome submissions from students, PhD researchers, and early-career scientists.

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.

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)