IN2P3/IRFU Machine Learning workshop 2025

Session

ML in Experimental Design and Control

design

27 nov. 2025, 13:30

G. Iltis (LPC) and Seminar room maison d'hôtes (GANIL) (LPC Caen and GANIL)

Présidents de session

ML in Experimental Design and Control

• Hayg Guler (IJCLAB)

170. AI for the Design and Operation of Particle Accelerators

Dr Adnan Ghribi ({CNRS}UPR3266)

27/11/2025 13:30

Accelerator control

Artificial intelligence (AI) is emerging today as a major driver of innovation for research infrastructures. In the field of particle accelerators, it opens up unprecedented opportunities for real-time control, predictive diagnostics, beam optimization, and the design of new devices. This presentation will provide an overview of current AI approaches and applications in accelerator science,...

162. Machine Learning for Next-Generation Gravitational Wave Observatories: The MANGO Project

Antsa Rasamoela (L2I Toulouse, CNRS/IN2P3, Université de Toulouse)

27/11/2025 13:55

Analysis : event classification, statistical analysis and inference, anomaly detection

The next generation of gravitational wave observatories—the Einstein Telescope (ET), Cosmic Explorer (CE), and LISA—will revolutionize astrophysics but present unprecedented data analysis challenges. LISA will detect tens of thousands of overlapping signals requiring simultaneous inference in high-dimensional Bayesian settings, while ground-based detectors will face thousands of overlapping...

156. Machine Learning for Event Reconstruction in the CMS Phase-2 High Granularity Calorimeter Endcap

Gamze Sokmen (LLR/CNRS)

27/11/2025 14:15

Object detection and reconstruction

The High-Luminosity LHC (HL-LHC) will provide unprecedented opportunities for precision measurements and new physics searches, but it will also bring extreme challenges for event reconstruction in the dense pile-up environment. To meet these challenges, the CMS detector is undergoing major upgrades, including the replacement of its endcap calorimeters with the High-Granularity Calorimeter...

147. Designing superconducting magnets using machine learning and evolutionary algorithms

Damien Minenna (CEA, Irfu)

27/11/2025 14:35

Simulations and surrogate models : replacing an existing complex physical model

We showcase recent advancements from the magnet laboratory at CEA/IRFU in designing superconducting magnets.

151. FAIR Universe Challenge and Benchmark

RAGANSU CHAKKAPPAI (IJCLab-Orsay)

27/11/2025 14:55

Training, courses, tutorials, open datasets and challenges

This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in $(H \rightarrow \tau^+ \tau^-)$ cross-section measurement. Participants were tasked with developing advanced analysis techniques capable of dealing with uncertainties in the input training data and providing credible confidence intervals. The accuracy of these intervals was...