EuCAIFCon 2025

Session

🌟 Highlight talks

16 Jun 2025, 15:30

THotel, Cagliari, Sardinia, Italy

Conveners

🌟 Highlight talks

• David Rousseau (IJCLab-Orsay)

🌟 Highlight talks

• Sven Krippendorf (DAMTP Cambridge)

🌟 Highlight talks

• James Alvey (University of Cambridge)

🌟 Highlight talks

• Verena Kain

119. 🌟 Fast, accurate, and precise detector simulation with vision transformers

Luigi Favaro

16/06/2025, 15:30

Simulations & Generative Models

Highlight talk

The speed and fidelity of detector simulations in particle physics pose compelling questions about LHC analysis and future colliders. The sparse high-dimensional data combined with the required precision provide a challenging task for modern generative networks. We present solutions with different tradeoffs, including accurate and precise Conditional Flow Matching and faster coupling-based...

125. 🌟 Harnessing AI and ML Innovations for High-Luminosity LHC: Transitioning from R&D to Production

Jan Stark (L2I Toulouse, CNRS/IN2P3, Université de Toulouse)

16/06/2025, 15:55

Inference & Uncertainty

Highlight talk

With the upcoming High-Luminosity Large Hadron Collider (HL-LHC) and the corresponding increase in collision rates and pile-up, a significant surge in data quantity and complexity is expected. In response, substantial R&D efforts in artificial intelligence (AI) and machine learning (ML) have been initiated by the community in recent years to develop faster and more efficient algorithms capable...

162. 🌟 Autonomous Fabry-Perot cavity locking via deep reinforcement learning

Dr Mateusz Bawaj (University of Perugia)

17/06/2025, 10:25

Real-Time Data Processing

Highlight talk

This work explores the application of Reinforcement Learning (RL) to the control of a Fabry-Perot (FP) optical cavity, a key component in interferometric gravitational-wave detectors. By leveraging RL’s inherent ability to handle high-dimensional non-linear systems, the project aims to achieve robust and autonomous cavity locking—a process typically hindered by elevated finesse values, mirror...

15. 🌟 Tests for model misspecification in simulation-based inference

Noemi Anau Montel (Max Planck Institute for Astrophysics)

17/06/2025, 10:50

Patterns & Anomalies

Highlight talk

Model misspecification analysis strategies, such as anomaly detection, model validation, and model comparison are a key component of scientific model development. Over the last few years, there has been a rapid rise in the use of simulation-based inference (SBI) techniques for Bayesian parameter estimation, applied to increasingly complex forward models. To move towards fully simulation-based...

91. 🌟 Towards foundation model for astrophysical source detection: An End-to-End Gamma-Ray Data Analysis Pipeline Using Deep Learning

Judit Pérez-Romero (CAC/UNG)

17/06/2025, 11:15

Foundation Models

Highlight talk

The increasing volume of gamma-ray data from space-borne telescopes, like Fermi-LAT, and the upcoming ground-based telescopes, like the Cherenkov Telescope Array Observatory (CTAO), presents us with both opportunities and challenges. Traditional analysis methods based on likelihood analysis are often used for gamma-ray source detection and further characterization tasks. A key challenge to...

14. 🌟 pop-cosmos: scaleable Bayesian inference of galaxy properties under a diffusion model prior

Stephen Thorp (Oskar Klein Centre, Stockholm University)

18/06/2025, 09:00

Inference & Uncertainty

Highlight talk

Projects such as the imminent Vera C. Rubin Observatory are critical tools for understanding cosmological questions like the nature of dark energy. By observing huge numbers of galaxies, they enable us to map the large scale structure of the Universe. However, this is only possible if we are able to accurately model our photometric observations of the galaxies, and thus infer their redshifts...

9. 🌟 Field-Level Emulation with Neural Networks

Drew Jamieson (Max-Planck Institute for Astrophysics)

18/06/2025, 09:25

Inference & Uncertainty

Highlight talk

Upcoming galaxy surveys promise to greatly inform our models of the Universe’s composition and history. Leveraging this wealth of data requires simulations that are accurate and computationally efficient. While N-body simulations set the standard for precision, their computational cost makes them impractical for large-scale data analysis. In this talk, I will present a neural network-based...

36. 🌟 Foundation models for HEP

Anna Hallin (Universität Hamburg)

19/06/2025, 11:00

Foundation Models

Highlight talk

Foundation models are machine learning models trained on large datasets, capable of being finetuned for a variety of downstream tasks. In a rather short time, several approaches to building foundation models for High energy physics have been presented. I will provide an overview of some of these approaches, and present some visions and open questions for the development of foundation models in HEP.

2. 🌟 AI for cosmic ray direct detection in space with the DAMPE mission

Andrii Tykhonov

19/06/2025, 11:25

Patterns & Anomalies

Highlight talk

DArk Matter Particle Explorer (DAMPE) is a pioneering instrument launched in space in 2015, designed for precise cosmic ray measurements reaching unprecedented hundreds of TeV in energy. One of the key challenges with DAMPE lies in cosmic ray data analysis at such high energies. It has been shown recently that deep learning can boost the experiment precision in regression (particle...