HAMLET-PHYSICS 2026 Conference

Session

Plenary: Experimental physics and detectors

20 Aug 2026, 10:20

Margrethe Bohr Salen (Niels Bohr Building)

46. Transformer-encoded normalizing flows and their application to directional reconstruction in IceCube

Thorsten Glu

20/08/2026, 10:20

The directional reconstruction of IceCube neutrinos, including the estimation of uncertainties, is essential for point source searches. In recent years, per-event posterior estimation with normalizing flows has emerged as a powerful reconstruction technique for the direction. The first part of the talk will give an introduction to normalizing flows. The second part will describe recent...

2. Building foundation models with low-level collision data in particle physics

Shreya Saha (Adelaide University)

20/08/2026, 10:40

Plenary

The integration of foundation models in particle physics is gaining pace rapidly and has expanded the search for new physics. This talk presents foundation models trained on low-level data from the first fully simulated dataset using Open Data Detector (ColliderML), to distinguish between Standard Model and Beyond Standard Model processes. We compare new physics discovery using only low level...

7. Model-agnostic searches for new physics with NPLM, and its reinterpretation

Jack Harrison (IFAE - Barcelona)

20/08/2026, 11:00

Plenary

With recent advances in NSBI techniques, specifically neural ratio estimation, Beyond the Standard Model searches can now exploit higher dimensional inputs in an unbinned way, boosting their sensitivity for both parameter estimation and new physics searches. The New Physics Learning Machine (NPLM) approach (D’Agnolo et al., 2305.10500) provides a framework to adapt these techniques into an...

47. Learning Standard Model structure from LHC data with Riemannian flow matching

Kevin Urquía (University of Copenhagen)

20/08/2026, 11:20

Can a generative model learn the structure of the Standard Model directly from collider data, without being told which resonances or correlations to expect? We present ShellFlow, a transformer-based Riemannian conditional flow-matching model trained on approximately one billion proton-proton collision events from the ATLAS 13 TeV Open Data release. Conditioned on the observed event...

43. Detector simulation for neural network position reconstruction in the i-RASE system

Michał Kossakowski (DTU Space), Rikke Stougaard Klausen (DTU Space)

20/08/2026, 11:40

Plenary

The MeV gamma-ray domain suffers from poor sensitivity, motivating advanced detector and readout technologies. The i-RASE (Intelligent RAdiation SEnsor readout systems) project addresses this by combining a custom low-noise front end with FPGA-implemented, physics-informed neural networks for real-time event reconstruction in 3D CdZnTe (CZT) Drift-Strip Detectors (DSD). At DTU Space, work...

26. Challenges of using machine learning in rare heavy-ion measurements

Ida Storehaug

20/08/2026, 12:00

Poster

Searching for rare B meson decays in heavy-ion collisions requires separating a small signal from a large and highly variable background. Machine-learning methods could improve this separation, but their training would rely largely on simulated signal and background samples that do not fully reproduce the real data. Differences in detector response, event multiplicity, and background...