ML4Jets 2026

Session

Plenary Presentations

14 Sept 2026, 09:30

C1 (Hörsaalzentrum University of Vienna)

Conveners

Plenary Presentations

• David Shih

Plenary Presentations

• David Rousseau (IJCLab, Université Paris-Saclay, CNRS/IN2P3)

Plenary Presentations

• Konstantin Matchev (University of Alabama (US))

Plenary Presentations

• Michael Kramer

Plenary Presentations

• Tilman Plehn (Heidelberg University)

151. ML for reconstruction: A journey through deep learning’s impact for LHC experiments

Nicole Michelle Hartman (TUM (DE))

14/09/2026, 09:30

The complexity of jet tagging and regression tasks has driven much of the ML4HEP developments in the past decade. From this wave of research, transformers have emerged as a general-purpose architecture for particle physics, which additionally benefit from the scale of our massive datasets. This talk reviews recent developments in jet tagging and scaling studies that allow the prediction of...

152. Machine Learning Before the Collision: AI for Theory and Phenomenology

Ben Assi, Ben Assi, Benoit Assi

14/09/2026, 10:00

Most machine learning at colliders operates downstream of a theory prediction: reconstruction, simulation, inference, and search all take a Standard Model expectation as input. This talk is about the other side — how AI is changing the calculations themselves. In scattering amplitudes, transformers and symbolic regression now assist bootstrap calculations, integral reduction, and the...

153. Machine Learning for LHC Event Generation

Theo Heimel (UCLouvain)

15/09/2026, 09:00

The high-luminosity LHC will collect an unprecedented amount of data, and we will need to match it with fast and precise theory predictions. In recent years, ML-based sampling and surrogates have become key technologies for achieving these goals within the available computational budget. In this talk, I will review the ongoing efforts to establish fully GPU- and ML-accelerated sampling at...

156. Overview of the Application of Generative Models to Fast Simulation in Large HEP Experiments

Michał Mazurek (National Centre for Nuclear Research (PL))

15/09/2026, 09:30

Monte Carlo simulations are essential for physics analyses and detector design in High Energy Physics (HEP). As the computational requirements of traditional simulation rapidly outpace available computing budgets, leveraging Generative AI for fast simulation has become vital to producing the required volume of simulated samples.
Nevertheless, transitioning from initial, toy models to...

157. Neural Simulation-Based Inference at the LHC

Jay Ajitbhai Sandesara (University of Wisconsin (US))

15/09/2026, 10:00

Neural Simulation-Based Inference (NSBI) is a family of emerging techniques that enable statistical inference with high-dimensional data, even when exact likelihoods are analytically intractable. These techniques use deep learning to build likelihood- or posterior-based inference models directly from simulations. By reducing reliance on hand-crafted, low-dimensional summary observables, NSBI...

155. Foundation Models for High Energy Physics

Anna Hallin (University of Hamburg)

16/09/2026, 09:00

1

Research into foundation models for high energy physics has moved fast beyond the initial prototyping efforts. As the community has been exploring input representations, pretraining strategies and downstream tasks, the number of models has skyrocketed. This talk will not give an overview of all those models; rather, I aim to give an overview of the underlying methods. From next token...

118. From Prompts to Publications: Agentic AI for Collider Analysis

Eric Anton Moreno (Massachusetts Inst. of Technology (US))

16/09/2026, 09:30

Experimental high energy physics analysis is traditionally a multi-year effort dominated by repetitive implementation that demands little physics insight. LLM-based AI agents can now autonomously execute substantial portions of this pipeline, collapsing the implementation bottleneck to roughly ten hours of wall-clock time. This talk surveys the rapidly developing landscape of agentic AI in...

150. Anomaly Detection: From Methods to Applications

Marie Hein (RWTH Aachen University)

17/09/2026, 09:00

Traditional new-physics searches face a trade-off between coverage and sensitivity. Anomaly detection offers a way to extend their reach by searching for unusual signatures without assuming a specific signal model. Over the past decade, the field has developed a broad range of anomaly-detection paradigms and methods. More recently, the focus has begun to shift from developing new methods...

154. Edge ML in Particle Physics: Bringing Inference to the Detector

Sioni Paris Summers (CERN)

17/09/2026, 09:30

Machine Learning for trigger/DAQ was among the first applications of ML in particle physics in the late 1980s. While great strides have been made since then in ML for physics, in analysis and reconstruction, ML at the edge has not been as prominent. Trigger/DAQ systems are a latency bound, custom electronics arena presenting challenging technical requirements for ML inference. Now, following...

158. GPU-Accelerated Online Reconstruction in ALICE

Felix Weiglhofer (CERN)

18/09/2026, 09:00

The ALICE detector at the LHC processed data rates of up to 1.2 TB/s
during Run 3. Real-time reconstruction of this continuous data stream is
essential, as the raw detector output is too large to be stored
permanently. ALICE therefore operates a heterogeneous computing farm
with 2800 GPUs, on which the most computationally demanding
reconstruction algorithms are executed.

This...

41. Machine Learning for Hadronization

Manuel Szewc

18/09/2026, 09:30

Hadronization, the transition between unobservable quarks and gluons to observable hadrons, is a key aspect of the theoretical framework of particle physics. However, it is a fundamentally challenging process due to its non-pertubative nature, and thus event generators implement empirical models based on QCD insights. In this talk, I'll detail how Machine Learning has been incorporated into...