Machine Learning for a new era of data-driven planetary science

The Europlanet 2024 Research Infrastructure (RI) project looks at the many ways Machine Learning (ML) is revolutionising planetary science. The advent of Machine Learning (ML) has enabled a new approach, known as data-driven science. Using the wealth of datasets and streams available, ML can explore the data to find a pattern or commonality. Out of these initial steps comes a hypothesis that can be tested through data analysis, which, again, hopefully leads to a new understanding. Clustering or fusing datasets, moreover, can reveal connections that are not recognisable in the individual datasets.

The Europlanet 2024 Research Infrastructure is a €10m project, funded by the European Commission’s Horizon 2020 programme, that supports the planetary science community. The project’s core activities are to provide access to facilities, field sites, and data services.

However, Europlanet also provides investment through ‘Joint Research Activities’ that combine the expertise of multiple partners to create the new infrastructure and services needed to carry out world-leading planetary research. Since 2020, the project has developed ML tools to handle complex planetary science data more efficiently and provide opportunities to combine and visualise multiple diverse datasets. This programme has been further enhanced through a collaboration with a second Horizon 2020 project, EXPLORE, which is developing applications for the exploitation of galactic, stellar and lunar data, and provides a platform for deploying and testing ML tools and services.

Further, Europlanet’s ML-powered tools are based on scientific cases proposed by the community that address key challenges in planetary research. From these proposals, seven cases were chosen to follow up initially during the project, and further cases have been added over time. All the tools are open-source, ready-to-use, and highly customisable, enabling other researchers to freely deploy and adapt them for their own research scenarios.

Lastly, it should be noted that, by developing ML tools tailored to data-driven planetary science, Europlanet has cemented collaborations, started to build new user communities and developed services that are already resulting in publications. While the planetary science community could be seen as late to the party in adopting ML, interest is now high. This couldn’t be more timely – with flagship missions to Mercury and Jupiter soon adding to the deluge of data streams, the era of data-driven science is only just beginning.

Europlanet 2024 RI and EXPLORE have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 871149 and No. 101004214, respectively.

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PhD Life – Part II: Finding and Starting the PhD

Stairway to Space
Stairway to Space
PhD Life - Part II: Finding and Starting the PhD

In our second episode dedicated to PhD life, we dig into the primary steps of a PhD, including searching and applications, timing and consequent interviews and tips for the Resume.

EPEC website:
Episode presentation: I. Di Pietro, F. Karakostas, E. Luzzi, M. Mirino, J.E. Silva
Production team: I. Di Pietro, F. Karakostas, E. Luzzi, M. Mirino, J.E. Silva, S. Tanbakouei, G. Tognon, J. Dias
© Europlanet Society 2022