AI4Science: A New Paradigm for Scientific Discovery
On March 9, Sorbonne University hosted the launch day of the AI4Science initiative within SCAI (Sorbonne Cluster for Artificial Intelligence). The event brought together researchers from multiple disciplines, institutional representatives, and industrial partners to explore how artificial intelligence is transforming scientific discovery. Beyond a mere overview of projects, the day highlighted a deeper evolution: the emergence of a new research paradigm where AI and scientific reasoning are increasingly and inextricably intertwined.
Following the success of our inaugural launch event, SCAI is proud to announce its first seminar.
The topic of the first session is "Symmetry-aware networks for Particle Physics
How equivariant transformers enable a deeper understanding of particle jets" - by Antoine Petitjean, AIPHY Team.
Given the scale of data available from experiments, particle physics is particularly well-suited to machine learning applications. Off-the-shelf ML models already outperform classical methods, but incorporating physics knowledge in our algorithms leads to better results. To study jets, sets of particles produced in high-energy collisions, we can leverage their known symmetry group with equivariant transformers for several tasks that parallel standard ML problems, notably classification and inverse problems. We introduce a Lorentz-equivariant architecture and how it can be tuned for those two tasks. For trigger-level classification, real-time jet tagging in the experiments, we propose different ways of improving the efficiency of our networks. To reconstruct particles from measurements, we introduce a sequential approach to solve this inverse problem with generative models.
To participate at the seminar, the registration is mandatory.