2026-07-20 –, Room 1.38 (Ground Floor, Turing)
The intrinsic difficulties related to gathering and processing astronomical data have traditionally branded it a “data starving” field. The situation changed only in the last few decades with the advent of large scale sky surveys that made publicly available the first extremely large and coherent astronomical data sets. In this context, Python has played a crucial role in enabling an extremely fast development of tools among the astronomical community, thus fostering an unprecedented revolution on how experts interact with their data. In this talk, I will describe the first stages of astronomical data analysis, how new data challenges were imposed in the last century and how Python was crucial to change the paradigm of astronomical data analysis. Finally, I will discuss the new framework of astronomical data and the impact of python developed tools in the process of scientific analysis. Finally, I will highlight the most challenging issues still to be faced in the era of the surveys like the Vera C. Rubin Observatory, as well as the impact of foundation models in the scientific exercise of astronomical discovery.
Invited Keynote talk.
The idea is to give an overview of the difficulties involving astronomical data analysis and the need to user friendly, rapid evolving and reliable software to enable this scientific exercise. I expect this talk to help the audience brainstorm why Python has been so successful among astronomers and plan to the future: can this pivotal role be kept in the era of foundation models? do we want it to? what are the lessons to be kept in mind and future challenges we will face in the next decades?
Research engineer at CNRS, France. Co-PI of the Fink Broker and co-founder of the SNAD collaboration and the Cosmostatistics Initiative (COIN). Work on the development of interdisciplinary science environments, machine learning applications to astronomy and adaptive learning techniques.