Training school on advanced data processing methods, 9-11 October 2024, Castelldefels/Barcelona, Spain

Training Goals & Topics

Data analysis and treatment is a key skill for experimental science. Beyond determination of the signal to noise of the detection scheme and applying an error analysis, there exist a plethora of methods to extract meaningful information from the measurement. Independent on the physical origin, the statistical significance of data determines which methods produce the most meaningful representation of the physics. Machine learning methods are just one example, others are nonlinear hypersurfaces for singular value decomposition methods. The goal of this training school is an introduction to the different methods, development of an understanding what can be used when, and some hands-on practicing with some examples. The underlying methods are applicable to spectroscopy or imaging.

Target Audience

The school is primarily directed towards PhD students from Laserlab-Europe partners. Postdoctoral researchers and/or local masters student undertaking a thesis in the field may also participate if there are places available.

Format

The 3-day school has 3 morning and 2 afternoon sessions that include practical classes, and scope for interaction among the participants, including the possibility of short-talks and / or posters from the students, lab visits, and social activities.

Preliminary List of Speakers

Abbas Ourmazd, UWM, Dynamics from Noisy Data with Extreme Timing Uncertainty
Martino Trassinelli, INSP, Bayesian data analysis tools for atomic physics
Philip Harris, MIT, Data Science in Physics
Nico Hoffmann, HZDR, AI for Future Photon Sciences