EuCAIFCon 2024

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Sessions*

2.2 Generative models & Simulation of physical systems

Parallel session

AI highlight

Plenary panel discussion

EuCAIF Workgroup summaries

Final keynote

Summary talks

Poster session

1.3 Simulation-based inference

1.4 Hardware acceleration & FPGAs

1.2 Generative models & Simulation of physical systems

EuCAIF WG

4.3 Physics-informed AI, Foundation models and related techniques

1.1 Pattern recognition & Image analysis

2.1 Pattern recognition & Image analysis

3.1 Pattern recognition & Image analysis

4.1 Pattern recognition, Image analysis & Uncertainty quantification

5.1 Generative models & Simulation of physical systems

2.3 Simulation-based inference

4.2 Simulation-based inference

2.4 Hardware acceleration & FPGAs

3.3 Hardware acceleration, FPGAs & Uncertainty quantification

3.2 Physics-informed AI & Integration of physics and ML

5.2 Physics-informed AI & Integration of physics and ML

3.4 Foundation models and related techniques

4.4 Explainable AI

5.4 Foundation models and related techniques, Variational inference

5.3 Uncertainty quantification, Pattern recognition and Simulation-based inference

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Contributions*

A deep learning method for the gamma-ray identification with the DAMPE space mission (in session "3.1 Pattern recognition & Image analysis")

A deep learning method for the trajectory reconstruction of gamma rays with the DAMPE space mission (in session "4.2 Simulation-based inference")

A fast convolutional neural network for online particle track recognition (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

A Hybrid Approach to Anomaly Detection in Particle Physics (in session "1.1 Pattern recognition & Image analysis")

A Neural-Network-defined Gaussian Mixture Model for particle identification in LHCb (in session "3.2 Physics-informed AI & Integration of physics and ML")

A Real-Time Tool for anomaly detection in Advanced Virgo's Auxiliary channels (in session "3.4 Foundation models and related techniques")

A Strong Gravitational Lens Is Worth a Thousand Dark Matter Halos: Inference on Small-Scale Structure Using Sequential Methods (in session "4.2 Simulation-based inference")

A surrogate model to optimize injection efficiency in PSI muEDM Experiment (in session "5.3 Uncertainty quantification, Pattern recognition and Simulation-based inference")

Accelerating the search for mass bumps using the Data-Directed Paradigm (in session "1.1 Pattern recognition & Image analysis")

Accelerator Physics and AI (in session "Summary talks")

Active Learning for Gravitational Wave modelling (in session "1.1 Pattern recognition & Image analysis")

Adaptive Machine Learning on FPGAs: Bridging Simulated and Real-World Data in High-Energy Physics (in session "1.4 Hardware acceleration & FPGAs")

Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC (in session "2.1 Pattern recognition & Image analysis")

Advancing Digital Transformation in Research on Universe and Matter in Germany (in session "2.1 Pattern recognition & Image analysis")

Advancing Generative Modelling of Calorimeter Showers on Three Frontiers (in session "2.2 Generative models & Simulation of physical systems")

AI ethics and fundamental physics (in session "AI highlight")

AI-driven discovery of charm quarks in the proton (in session "5.3 Uncertainty quantification, Pattern recognition and Simulation-based inference")

AI-Driven Exploration of Strongly Interacting Nuclear Matter under Extreme Conditions (in session "5.1 Generative models & Simulation of physical systems")

Ameliorating transient noise bursts in gravitational-wave searches for intermediate-mass black holes (in session "1.1 Pattern recognition & Image analysis")

Analyzing ML-enabled Full Population Model for Galaxy SEDs with Unsupervised Learning and Mutual Information (in session "4.2 Simulation-based inference")

Anomaly aware machine learning for dark matter direct detection at DARWIN (in session "2.3 Simulation-based inference")

Anomaly detection search for BSM physics in ATLAS experiment at LHC (in session "4.3 Physics-informed AI, Foundation models and related techniques")

Application of science-informed AI in experimental particle physics and neuroscience (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Applying hierarchical autoregressive neural networks for three-dimensional Ising model (in session "4.3 Physics-informed AI, Foundation models and related techniques")

Artificial Intelligence techniques in KM3NeT (in session "1.3 Simulation-based inference")

Astroparticle Physics and AI (in session "Summary talks")

Attention to the strengths of physics interactions: Enhanced Deep Learning Event Classification for Particle Physics Experiments (in session "3.2 Physics-informed AI & Integration of physics and ML")

b-hive: a modular training framework for state-of-the-art object-tagging within the python ecosystem at the CMS experiment (in session "4.4 Explainable AI")

Boosted object reconstruction with Monte-Carlo truth supervised Graph Neural Networks (in session "1.1 Pattern recognition & Image analysis")

Building sparse kernel methods via dictionary learning. Expressive, regularized and interpretable models for statistical anomaly detection (in session "1.3 Simulation-based inference")

Calculating entanglement entropy with generative neural networks (in session "2.2 Generative models & Simulation of physical systems")

Calibrating Bayesian Tension Statistics with Neural Ratio Estimators (in session "1.3 Simulation-based inference")

CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds (in session "2.2 Generative models & Simulation of physical systems")

Characterizing the Fermi-LAT high-latitude sky with simulation-based inference (in session "2.3 Simulation-based inference")

Characterizing the High Energy Gamma-ray Sources using Deep Learning (in session "3.1 Pattern recognition & Image analysis")

Choose Your Diffusion: Efficient and flexible way to accelerate the diffusion model dynamics in fast physics simulation (in session "2.2 Generative models & Simulation of physical systems")

Clustering Considerations for Nested Sampling (in session "2.3 Simulation-based inference")

Convolutional neural network search for long-duration transient gravitational waves from glitching pulsars (in session "4.2 Simulation-based inference")

Cosmology and AI (in session "Summary talks")

COSMOPOWER: fully-differentiable Bayesian cosmology with neural emulators (in session "2.3 Simulation-based inference")

Costless Performance Gains in Nested Sampling for Applications to AI and Gravitational Waves (in session "2.1 Pattern recognition & Image analysis")

Deep Learning for Cosmic-Ray Observatories (in session "2.4 Hardware acceleration & FPGAs")

Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Deep learning techniques in the study of the hypertriton puzzle (in session "3.1 Pattern recognition & Image analysis")

Deep Learning-Based Data Processing in Large-Sized Telescopes of the Cherenkov Telescope Array: FPGA Implementation and Performance Comparison with GPUs (in session "1.4 Hardware acceleration & FPGAs")

Deep support vector data description models on an analog in-memory computing platform for real-time unsupervised anomaly detection. (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Differentiable Vertex Fitting for Jet Flavour Tagging (in session "1.2 Generative models & Simulation of physical systems")

Diffusion meets Nested Sampling (in session "2.2 Generative models & Simulation of physical systems")

Doubling the Detection Rate of Ultra-High Energy Neutrinos through a Neural Network Trigger (in session "2.4 Hardware acceleration & FPGAs")

Efficient Parameter Space Exploration in BSM Theories with Batched Multi-Objective Constraint Active Search (in session "2.1 Pattern recognition & Image analysis")

Einstein Telescope: binary black holes gravitational wave signals detection from three detectors combined data using deep learning (in session "2.1 Pattern recognition & Image analysis")

Embedded Neural Networks on FPGAs for Real-Time Computation of the Energy Deposited in the ATLAS Liquid Argon Calorimeter (in session "2.4 Hardware acceleration & FPGAs")

Emulation by committee: faster AGN fitting (in session "2.2 Generative models & Simulation of physical systems")

End-to-End Object Reconstruction in a Sampling-Calorimeter using YOLO (in session "3.1 Pattern recognition & Image analysis")

Energy-based graph autoencoders for semivisible jet tagging in the Lund representation (in session "4.4 Explainable AI")

Enhancing Electron Identification Using RCNet: A Deep CNN Approach for RICH Ring Reconstruction (in session "3.1 Pattern recognition & Image analysis")

Enhancing Robustness: BSM Parameter Inference with n1D-CNN and Novel Data Augmentation (in session "2.3 Simulation-based inference")

Estimating classical mutual information for spin systems and field theories using generative neural networks (in session "2.2 Generative models & Simulation of physical systems")

Estimation of Machine Learning model uncertainty in particle physics event classifiers (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Evaluating Generative Models with non-parametric two-sample tests (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Experimental particle physics and AI (in session "Summary talks")

Explainable deep learning models for cosmological structure formation (in session "4.4 Explainable AI")

Exploration of QCD matter under extreme conditions meets machine learning (in session "1.2 Generative models & Simulation of physical systems")

Exploring the Universe with Radio Astronomy and AI (in session "1.2 Generative models & Simulation of physical systems")

Extracting Dark Matter Halo Parameters with Overheated Exoplanets (in session "1.3 Simulation-based inference")

Fair Universe HiggsML Uncertainty Challenge (in session "5.3 Uncertainty quantification, Pattern recognition and Simulation-based inference")

Fast Inference of Deep Learning Models with SOFIE (in session "2.4 Hardware acceleration & FPGAs")

Feature selection techniques for CR isotope identification with the AMS-02 experiment in space (in session "2.1 Pattern recognition & Image analysis")

Finetuning Foundation Models for Joint Analysis Optimization (in session "4.3 Physics-informed AI, Foundation models and related techniques")

FlashSim: an end-to-end fast simulation prototype using Normalizing Flow (in session "3.2 Physics-informed AI & Integration of physics and ML")

Flavour Tagging with Graph Neural Networks with the ATLAS experiment (in session "3.4 Foundation models and related techniques")

Flexible conditional normalizing flow distributions over manifolds: the jammy-flows toolkit (in session "4.2 Simulation-based inference")

Flow-based generative models for particle calorimeter simulation (in session "2.2 Generative models & Simulation of physical systems")

Full-event reconstruction using CNN-based models on calibrated waveforms for the Large-Sized Telescope prototype of the Cherenkov Telescope Array (in session "2.4 Hardware acceleration & FPGAs")

Fully Bayesian Forecasts with Neural Bayes Ratio Estimation (in session "2.3 Simulation-based inference")

Galaxy redshift estimations with transfer and multi-task learning (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Gaussian processes for managing model uncertainty in gravitational wave analyses (in session "2.4 Hardware acceleration & FPGAs")

Generating Lagrangians for particle theories (in session "3.2 Physics-informed AI & Integration of physics and ML")

Generative models and lattice field theory (in session "1.2 Generative models & Simulation of physical systems")

Generative models for transient noise studies in Gravitational Waves detectors (in session "5.1 Generative models & Simulation of physical systems")

Generic representations of jets at detector-level with self-supervised learning (in session "3.4 Foundation models and related techniques")

GNN for Λ Hyperon Reconstruction in the WASA-FRS Experiment (in session "3.1 Pattern recognition & Image analysis")

Gradient-Annihilated PINNs for Solving Riemann Problems: Application to Relativistic Hydrodynamics (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Graph Neural Networks for charged-particle track reconstruction (in session "2.1 Pattern recognition & Image analysis")

Gravitational wave physics and AI (in session "Summary talks")

Hardware implementation of quantum machine learning predictors for ultra-low latency applications (in session "1.4 Hardware acceleration & FPGAs")

Hybrid quantum graph neural networks for particle tracking in high energy physics (in session "1.4 Hardware acceleration & FPGAs")

Hyperparameter optimization of neural networks for proton structure analyses (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Importance nested sampling with normalizing flows for gravitational-wave inference (in session "1.2 Generative models & Simulation of physical systems")

Improved Fixed Point Actions from Gauge Equivariant Neural Networks (in session "5.2 Physics-informed AI & Integration of physics and ML")

Improving Two-Neutron Detection Efficiency on the NEBULA Detector using XGBoost Algorithm (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Increasing the model agnosticity of weakly supervised anomaly detection (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Integrating Explainable AI in Modern High-Energy Physics (the MUCCA Project) (in session "5.2 Physics-informed AI & Integration of physics and ML")

interTwin - an interdisciplinary Digital Twin Engine for Science (in session "2.1 Pattern recognition & Image analysis")

JERALD: high-resolution dark matter and baryonic maps from cheap N-body simulations (in session "4.3 Physics-informed AI, Foundation models and related techniques")

Kicking it Off(-shell) with Direct Diffusion (in session "1.2 Generative models & Simulation of physical systems")

Learning new physics with a (kernel) machine (in session "1.1 Pattern recognition & Image analysis")

Learning the ‘Match’ Manifold to Accelerate Template Bank Generation (in session "3.4 Foundation models and related techniques")

Leveraging Physics-Informed Graph Neural Networks for Enhanced Combinatorial Optimization (in session "1.1 Pattern recognition & Image analysis")

LHC Event Generation with JetGPT (in session "3.4 Foundation models and related techniques")

Long-Lived Particles Anomaly Detection with Parametrized Quantum Circuits (in session "1.4 Hardware acceleration & FPGAs")

Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics (in session "3.2 Physics-informed AI & Integration of physics and ML")

Machine Learning applications at the ATLAS experiment (in session "2.1 Pattern recognition & Image analysis")

Machine learning for lattice field theory and back (in session "1.2 Generative models & Simulation of physical systems")

Machine learning for radiometer calibration in global 21cm cosmology (in session "1.3 Simulation-based inference")

Machine Learning-based Data Compression (in session "2.4 Hardware acceleration & FPGAs")

Machine-learning analysis of cosmic-ray nuclei data from the AMS-02 experiment (in session "3.2 Physics-informed AI & Integration of physics and ML")

Magnet Design Optimisation with Supervised Deep Neural Networks (in session "1.1 Pattern recognition & Image analysis")

Masked particle modelling (in session "4.3 Physics-informed AI, Foundation models and related techniques")

Methods in AI for Science (in session "AI highlight")

ML-based Unfolding Techniques for High Energy Physics (in session "1.3 Simulation-based inference")

Model compression and simplification pipelines for fast and explainable deep neural network inference in FPGAs in HEP (in session "1.4 Hardware acceleration & FPGAs")

Model selection with normalizing flows (in session "4.4 Explainable AI")

Modeling blazar broadband emission with convolutional neural network (in session "3.2 Physics-informed AI & Integration of physics and ML")

Multi-class classification of gamma-ray sources and the nature of excess of GeV gamma rays near the Galactic center (in session "4.4 Explainable AI")

Networks Learning the Universe: From 3D (cosmological inference) to 1D (classification of spectra) (in session "2.3 Simulation-based inference")

New developments and applications of a Deep-learning-based Full Event Interpretation (DFEI) in proton-proton collisions (in session "5.3 Uncertainty quantification, Pattern recognition and Simulation-based inference")

Next generation cosmological analysis with a re-usable library of machine learning emulators across a variety of cosmological models (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Next-Generation Background Removal for Astronomical Images based on Diffusion Models (in session "3.1 Pattern recognition & Image analysis")

Normalising flows for dense matter equation of state inference from gravitational wave observations of neutron star mergers (in session "4.2 Simulation-based inference")

Normalizing flows for jointly predicting photometry and photometric redshifts (in session "1.2 Generative models & Simulation of physical systems")

Nuclear Physics and AI (in session "Summary talks")

OmniJet-𝛼: The first cross-task foundation model for particle physics (in session "4.3 Physics-informed AI, Foundation models and related techniques")

Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN (in session "4.2 Simulation-based inference")

Optimizing bayesian inference in cosmology with Marginal Neural Ratio Estimation (in session "2.3 Simulation-based inference")

Outlook: AI for fundamental physics (in session "Final keynote")

Panel discussion I: Directions in AI and fundamental physics (in session "Plenary panel discussion")

Panel discussion II: AI Infrastructure (in session "Plenary panel discussion")

Panel discussion III: EuCAIF - Building a European Coalition for AI in Fundamental physics (in session "Plenary panel discussion")

Parameter estimation from quantum-jump data using neural networks (in session "1.4 Hardware acceleration & FPGAs")

Physics-Informed Neural Networks for Gravitational Waves (in session "1.2 Generative models & Simulation of physical systems")

PolySwyft: a sequential simulation-based nested sampler (in session "1.3 Simulation-based inference")

pop-cosmos: comprehensive forward modelling of photometric galaxy survey data (in session "1.3 Simulation-based inference")

Poster session A (in session "Poster session")

Poster session B (in session "Poster session")

Precision-Machine Learning for the Matrix Element Method (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Prospects for AI in physics and astronomy (in session "AI highlight")

Quantum and classical methods for ground state optimisation in quantum many-body problems (in session "1.4 Hardware acceleration & FPGAs")

Quantum Computing for Track Reconstruction at LHCb (in session "2.1 Pattern recognition & Image analysis")

Quantum Probabilistic Diffusion Models (in session "1.2 Generative models & Simulation of physical systems")

Quark/gluon discrimination and top tagging with dual attention transformer (in session "3.4 Foundation models and related techniques")

Quark/gluon tagging in CMS Open Data with CWoLa and TopicFlow (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Real-Time Detection of Low-Energy Events with 2DCNN on FPGA's for the DUNE Data Selection System (in session "1.4 Hardware acceleration & FPGAs")

Real-time Gravitational Wave Data Analysis with Machine Learning (in session "5.2 Physics-informed AI & Integration of physics and ML")

Realtime Anomaly Detection with the CMS Level-1 Global Trigger Test Crate (in session "2.4 Hardware acceleration & FPGAs")

Reconstructing dynamics and masses from gravitational waveforms (in session "4.4 Explainable AI")

Reconstructing the Hubble function with physics-informed neural networks (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Reconstructing the Neutron Star Equation of State with Bayesian deep learning (in session "4.4 Explainable AI")

Reconstruction of cosmological initial conditions with sequential simulation-based inference (in session "5.1 Generative models & Simulation of physical systems")

Reconstruction of Low Mass Vector Mesons via Dimuon decay channel using Machine Learning Technique for the CBM Experiment at FAIR (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Reinforcement learning for automatic data quality monitoring in HEP experiments (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Robust Uncertainty Quantification in Parton Distribution Function Inference (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Scientific Discovery from Ordered Information Decomposition (in session "5.4 Foundation models and related techniques, Variational inference")

Searches for exotic objects among Fermi-LAT gamma-ray sources with weakly supervised machine learning (in session "4.4 Explainable AI")

Searching for Dark Matter Subhalos in Astronomical Data using Deep Learning (in session "1.1 Pattern recognition & Image analysis")

Searching for gravitational waves from stellar-mass binary black holes early inspiral (in session "1.1 Pattern recognition & Image analysis")

Self-supervision for data-driven anomaly detection at the LHC (in session "5.4 Foundation models and related techniques, Variational inference")

Sensitivity of strong lenses to substructure with machine learning (in session "4.1 Pattern recognition, Image analysis & Uncertainty quantification")

Simulation Based Inference from the CD-EoR 21-cm signal (in session "4.2 Simulation-based inference")

Simulation of Z2 model using Variational Autoregressive Network (VAN). (in session "1.3 Simulation-based inference")

Simulation-Based Supernova Ia Cosmology (in session "2.3 Simulation-based inference")

Stochastic Gravitational Wave Background Analysis with SBI (in session "2.3 Simulation-based inference")

Studies on track finding algorithms based on machine learning with GPU and FPGA (in session "1.4 Hardware acceleration & FPGAs")

Studying Adversarial Deep Learning techniques in the context of High-Energy Physics (in session "3.3 Hardware acceleration, FPGAs & Uncertainty quantification")

Symbolic regression for precision LHC physics (in session "4.4 Explainable AI")

The Calorimeter Pyramid: Rethinking the design of generative calorimeter shower models (in session "2.2 Generative models & Simulation of physical systems")

The flash-simulation of the LHCb experiment using the Lamarr framework (in session "3.4 Foundation models and related techniques")

The MadNIS Reloaded (in session "2.2 Generative models & Simulation of physical systems")

Theoretical high-energy physics and AI (in session "Summary talks")

Towards the first time ever measurement of the $gg\rightarrow ZH$ process at the LHC using Transformer networks (in session "3.4 Foundation models and related techniques")

Transformer-inspired models for particle track reconstruction (in session "3.4 Foundation models and related techniques")

Tuning neural posterior estimation for gravitational wave inference (in session "4.2 Simulation-based inference")

Turning optimal classifiers into anomaly detectors (in session "3.2 Physics-informed AI & Integration of physics and ML")

Understanding galaxy cluster evolution with contrastive learning (in session "3.2 Physics-informed AI & Integration of physics and ML")

Unsupervised Classification of Radio Sources Through Self-Supervised Representation Learning (in session "4.3 Physics-informed AI, Foundation models and related techniques")

Unsupervised tagging of semivisible jets with energy-based autoencoders in CMS (in session "4.4 Explainable AI")

Using ML based Unfolding to reduce error on lattice QCD observables (in session "4.3 Physics-informed AI, Foundation models and related techniques")

Utilizing Artificial Intelligence Technologies for the Enhancement of X-ray Spectroscopy with Metallic-Magnetic Calorimeters (in session "3.4 Foundation models and related techniques")

Utilizing machine learning for the Data Analysis of AGATA’s PSA database. (in session "2.4 Hardware acceleration & FPGAs")

Validating Explainable AI Techniques through High Energy Physics Data (in session "3.2 Physics-informed AI & Integration of physics and ML")

WG 1 discussion (in session "EuCAIF WG")

WG 2 discussion (in session "EuCAIF WG")

WG 3 discussion (in session "EuCAIF WG")

WG 4 (in session "EuCAIF WG")

WG 5 discussion (in session "EuCAIF WG")

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