IN2P3/IRFU Machine Learning workshop 2026

Programme Scientifique

AI for Physics Analysis, Inference and Discovery

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

Examples include:
- Statistical inference and scientific interpretation
- Parameter estimation and biases
- New physics searches
- Scientific time-series analysis
- Event/object classification and selection
- Simulated Based Inference methods

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
- Emulators, surrogate models, digital twins

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

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

Event/Object Reconstruction and Identification

Machine learning methods for reconstructing particles, events, and astrophysical or cosmological observables from detector data.

Examples include:
- Particle, event and object reconstruction
- Image analysis and correction of systematics
- Tracking, clustering, jet tagging and particle identification
- Catalogue reduction and classification
- Multi-detector information fusion

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

AI for Scientific Instruments and Accelerators

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 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