IN2P3/IRFU Machine Learning workshop 2026

Programme Scientifique

Event Reconstruction and Particle Identification

Machine learning methods for reconstructing particles, events, and physical observables from detector data.

Examples include:
- Particle and event reconstruction
- Tracking and clustering
- Jet tagging and particle identification
- Multi-detector information fusion
- Reconstruction in high-granularity detectors

Detector Signal Processing, Calibration and Reconstruction

AI methods for detector-level signal processing and detector response modeling.

Examples include:
- Signal reconstruction and denoising
- Waveform analysis and deconvolution
- Detector calibration and alignment
- Differentiable calibration
- Detector response correction

Real-Time AI, Trigger and Edge Computing

Machine learning methods operating under latency, bandwidth, and hardware constraints.

Examples include:
- Trigger systems
- FPGA and embedded AI
- Online event selection
- Fast inference
- AI at the edge
- Resource-aware machine learning

Foundation Models, Representation Learning and Generative AI

General-purpose learning paradigms for scientific data and physics applications.

Examples include:
- Self-supervised learning
- Foundation models
- Representation learning
- Transformers
- Graph representation learning
- Generative AI and diffusion models
- Physics-informed learning

AI for Scientific Instruments, Accelerators and Digital Twins

AI methods for the design, optimization, operation, and control of scientific facilities.

Examples include:
- Accelerator diagnostics and control
- Detector and instrument optimization
- Surrogate models
- Digital twins
- Experimental design
- Scientific control systems

AI for Physics Analysis, Inference and Discovery

Machine learning methods supporting scientific measurements, interpretation, and discoveries.

Examples include:
- Statistical inference
- Parameter estimation
- New physics searches
- Scientific time-series analysis
- Event classification
- Scientific interpretation

Trustworthy AI for Physics

Methods improving the reliability, interpretability, and reproducibility of AI-based analyses.

Examples include:
- Uncertainty quantification
- Probabilistic machine learning
- Explainability and interpretability
- Robustness and domain adaptation
- AI validation and benchmarking
- Physics-informed constraints

AI Ecosystem: Infrastructure, Open Science and Community

Tools, infrastructures, datasets, and community initiatives enabling AI research in physics.

Examples include:
- Research software
- AI frameworks and workflows
- HPC and distributed computing
- FAIR data and open science
- Benchmark datasets and challenges
- Community initiatives and training