-
Date d'ouverture
-
Date limite de dépôt
Call for abstracts
Artificial Intelligence is transforming experimental physics, from detector design and operation to event reconstruction, data analysis, scientific discovery, and large-scale research infrastructures. Advances in deep learning, foundation models, probabilistic methods, and scientific AI are opening new opportunities across particle physics, nuclear physics, astroparticle physics, accelerator science, and related domains.
This workshop brings together researchers from IN2P3, CEA-Irfu, and the broader French scientific community to present recent developments, exchange ideas, and foster new collaborations in AI for physics.
We invite abstracts for oral presentations describing recent advances, work in progress, innovative ideas, software developments, benchmark datasets, and emerging applications of AI in physics. We particularly encourage contributions presenting ongoing research, early-stage developments, and new directions, in addition to published results.
Please select the track that best matches your contribution: lightning talk (5min) ou standard talk (20min).
If there are too many standard talks requested, we may reschedule some of them to lightning ones.
Note: Results from large collaborations may require approval from the relevant collaboration board or national contact. Since this is a national workshop, presentations by students and early-career researchers on ongoing work may be possible; please consult your collaboration's national contact if approval is required.
Tracks
1. 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
2. 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
3. Real-Time AI, Trigger and Edge Computing
Machine learning 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 ML
4. Foundation Models, Representation Learning and Generative AI
General-purpose learning paradigms for scientific data.
Examples include:
- Self-supervised learning
- Foundation models
- Representation learning
- Transformers
- Graph representation learning
- Generative AI and diffusion models
- Physics-informed learning
5. AI for Scientific Instruments, Accelerators and Digital Twins
AI 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
6. AI for Physics Analysis, Inference and Discovery
Machine learning methods supporting scientific measurements and discoveries.
Examples include:
- Statistical inference
- Parameter estimation
- New physics searches
- Scientific time-series analysis
- Event classification
- Scientific interpretation
7. Trustworthy AI for Physics
Methods that improve 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
8. 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