HAMLET-PHYSICS 2026 Conference

Session

Plenary: Fundamental physics, inference, theory

19 Aug 2026, 10:50

Margrethe Bohr Salen (Niels Bohr Building)

8. Speeding up Analytic Calculations in Theoretical Particle Physics via Machine Learning

Dr Matthias Wilhelm (NBI)

19/08/2026, 10:50

Plenary

In this talk, we will present recent progress on applying machine-learning techniques to speed up calculations in theoretical physics, in which we desire exact and analytic results. One example are so-called integration-by-parts reductions of Feynman integrals, which pose a frequent bottleneck in state-of-the-art calculations in theoretical particle and gravitational-wave physics. These...

6. Normalising flows for all-orders QED corrections in lattice field theory

Nils Hermansson-Truedsson (University of Edinburgh)

19/08/2026, 11:10

Plenary

Many low-energy precision tests of hadronic observables in the Standard Model now require the inclusion of isospin-breaking corrections due to light-quark mass differences and electromagnetism (QED). Isospin breaking typically appears for any prediction with better than percent-level precision, where examples are particle decay rates, hadron mass differences and the muon anomalous magnetic...

48. Next-token-prediction for Arbitrary Particle Physics Tasks

Daniel Murnane (Niels Bohr Institute, University of Copenhagen)

19/08/2026, 11:30

Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We ask whether one model can serve many of those steps at once, while still producing the intermediate objects (tracks, calorimeter cells, clusters, particles and jets) that make the chain interpretable. We represent every object in an event in a single shared token...

18. Hyperparameter Transfer: A Recipe for Efficient and Robust Scaling

Gage DeZoort (Princeton University)

19/08/2026, 11:50

Plenary

The performance of deep learning models is sensitive to the settings of various hyperparameters, most importantly learning rate and initialization scale. Hyperparameter tuning becomes expensive at scale, motivating the modern paradigm of hyperparameter transfer, a strategy for inferring near-optimal hyperparameters in larger models from their smaller counterparts, which are cheaper to...

16. Unfolding - Quantification learning for physics applications

Lukas Liland (TU Dortmund)

19/08/2026, 12:10

Poster

Any physics experiment consists, in essence, of collecting times, locations and charges. The quantity of interest that the experiment measures must be derived from these three basic ones, for example via a neural network that takes these data as input and produces an estimate of the value in concern as output. However, the true distribution of the quantity in interest is convolved (or folded)...

22. MultiCWoLa

Johann Ioannou-Nikolaides (NBI)

19/08/2026, 12:15

Poster

In many classification problems, reliable instance-level labels are unavailable. However, it is often possible to construct weakly enriched unlabeled samples: datasets selected by different cuts, sources, populations, or experimental conditions that change latent class proportions without revealing them. Classification without Labels (CWoLa) shows that, in the binary case ($K=2$), a classifier...

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)

19/08/2026, 12:20

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)

19/08/2026, 12:25

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...