HAMLET-PHYSICS 2026 Conference

Session

Plenary: Experimental physics and detectors

20 Aug 2026, 10:30

Margrethe Bohr Salen (Niels Bohr Building)

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

Shreya Saha (Adelaide University)

20/08/2026, 10:30

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...

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, 10:50

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...

15. Differentiable End-to-End Optimization of In-Ice Radio Neutrino Detectors

Martin Ravn

20/08/2026, 11:10

Plenary

The design choices made for particle physics detectors can have long-lasting impacts on the scientific outcome of a given experiment. Optimizing detector layouts is therefore essential in the planning stages of an experiment. However, conventional brute-force Monte Carlo–based studies often become computationally prohibitive when exploring large design parameter spaces. Differentiable...

19. Photonic imaging, sensing, and detection assisted by AI

Miroslav Ježek (Palacký University Olomouc)

20/08/2026, 11:30

Plenary

Light carries rich physical information, but each detected photon can be costly in optical power, sample dose, or acquisition time. Modern photonic sensing, quantum technologies, and nanoscale imaging therefore require not only high sensitivity, but also maximal information efficiency. This talk reviews recent results from our group on AI-assisted photonic sensing and quantum detection, with...

29. Performance of the Boosted Decision Tree for $\pi^0$ Reconstruction in KLOE Data

Dr Bo Cao (Department of Physics and Astronomy; Nuclear Physics, Uppsala University)

20/08/2026, 11:50

Poster

We present a Gradient-Boosted Decision Tree (BDT) for $\pi^0$ reconstruction in the $e^+e^- \to \pi^+\pi^-\pi^0\gamma$ process at the KLOE experiment. Trained on Monte Carlo events using ten kinematic variables and a series of preselection criteria (including $\chi^2 < 100$), the model is optimized via Bayesian hyperparameter search. On independent validation samples, the BDT achieves an AUC...

13. Enhancing Detector Alignment with Machine Learning

Stefan Cretu (Lund University)

20/08/2026, 11:55

Poster

Precise detector alignment is essential for reliable charged-particle tracking in high-energy physics experiments. Standard track-based alignment methods typically depend on iterative global fits which can become computationally demanding for large and highly segmented detectors.

We investigate a machine-learning approach in which detector sensors are represented as nodes in a graph, while...

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...

28. Machine Learning Extraction of Flow Harmonics and Jet Amplitudes from Two-Particle Correlations in Pb-Pb Collisions at √(〖s_NN〗_ ) = 2.76 TeV

olja dordic (university of banjaluka)

20/08/2026, 12:05

Poster

We present a machine learning approach for the simultaneous extraction of anisotropic flow harmonics (v₁–v₆) and jet amplitudes from two-particle angular correlation functions in heavy-ion collisions. A multi-layer perceptron (MLP) neural network is trained on HIJING-like toy model simulations incorporating realistic physics ingredients, including collective flow parametrizations from ALICE...