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...

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

Jack Harrison (IFAE - Barcelona)

19/08/2026, 11:30

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...

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)...

32. Anomaly detection, BDTs and you: How the statistical properties of Boosted Decision Trees interact with physics

Sten Åstrand (Lund University)

19/08/2026, 12:15

Poster

In particle physics, papers on weakly supervised searches for new physics have started accumulating numbers. This is the paradigm of trying to learn the differences between a data-driven background estimate and real data, in order to isolate some small contribution of anomalous data points - the signal - in the real data. The technique has been shown to work in simulation, and has been applied...

22. MultiCWoLa

Johann Ioannou-Nikolaides (NBI)

19/08/2026, 12:20

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...