Machine Learning for Astrophysics 2024: Proceedings of the 2nd ML4ASTRO International Conference 8-12 July 2024 (Astrophysics and Space Science Proceedings, 62) - Hardcover

Hardcover

ISBN 10: 3032022312 ISBN 13: 9783032022318

Publisher: Springer, 2026

Synopsis

This proceedings book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.

About this title

About the Author

Simone Riggi (Ph.D. in Physics) has been a Research Data Scientist at the Istituto Nazionale di Astrofisica (INAF) since 2012. His work has primarily focused on scientific data analysis and visualization, machine learning, distributed computing, instrumentation simulation, monitoring and control, and system engineering. He has contributed to large research and technological projects in the fields of radio astronomy, high-energy cosmic rays, and applied physics, such as the Pierre Auger Observatory experiment, the Muon Portal project, and various European H2020 projects (AENEAS, NEANIAS). Currently, he is involved in the design and construction phase of the Square Kilometer Array (SKA) telescope and in the Galactic science programs carried out within the ASKAP-EMU and MeerKAT-GPS surveys. In these contexts, he is responsible for the monitoring and control system of SKA-Mid antennas and for developing radio source analysis tools using machine learning techniques and multi-wavelength data. 

Filomena Bufano (Ph.D. in Astronomy) has been a research staff scientist at Istituto Nazionale di Astrofisica (INAF) since 2016. Her scientific interests have been mainly focused on the study of massive stars evolution, in particular on their final stages. Promoting a multi-wavelength approach in her studies, she worked using data from different telescopes/surveys from UV to radio frequencies and has been a member of numerous international collaborations and projects. In view of the approaching era of a deluge of data expected from new ground and space-based facilities, she acquired deep skills in the use of machine learning algorithms and took part to important European projects aimed at the diffusion of such algorithm application to astrophysical data. Nowadays, she is involved in the preliminary activities of the Square Kilometre Array focussed on the Galactic Plane and in the Early Science Data Analysis phase of two important pathfinder/precursors of SKA: ASKAP and MeerKAT. 

Eva Sciacca (Ph.D. in Mathematics for Technology) is a Computer Scientist and Information Technology researcher with over a decade of experience, working at the Istituto Nazionale di Astrofisica (INAF) since 2012. She has been extensively involved in cutting-edge research activities in the field of big-data, visual analytics, and machine learning. She has been instrumental in facilitating astrophysical data processing on distributed computing infrastructures, with a special focus on High-Performance Computing (HPC) and Cloud Computing. Over the past five years, Eva has played a pivotal role in several European-funded projects, including VIALACTEA, INDIGO-DataCloud, AENEAS, EOSC-Pilot, NEANIAS, and SPACE. She has been at the forefront of harnessing the potential of the European Open Science Cloud (EOSC) and the European High-Performance Computing Joint Undertaking (EuroHPC JU) to advance scientific research, and she is actively involved in the IT activities of the Square Kilometre Array (SKA) Regional Centres.

From the Back Cover

This proceedings book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.

Edition details

• Publisher: Springer
• Publication date: 2026
• Language: English
• ISBN 10: 3032022312
• ISBN 13: 9783032022318
• Binding: Hardcover
• Number of pages: 205
• Editor: Bufano Filomena, Sciacca Eva, Riggi Simone