HAMLET-PHYSICS 2026 Conference

Session

Plenary: Climate, Earth, Environment

21 Aug 2026, 13:05

Margrethe Bohr Salen (Niels Bohr Building)

4. Deterministic Collapse and Stochastic Recovery in a Data-Driven Model of the Atlantic Meridional Overturning Circulation

Qi-fan Wu

21/08/2026, 13:05

Plenary

During the last ice-age, temperatures in Greenland have frequently increased and decreased by 10$^{\circ}$C. Detailed studies with climate models suggest that this is caused by collapses and recoveries of the Atlantic Meridional Overturning Circulation (AMOC). The causes of these AMOC transitions are still debated. Here we describe the development of a neural-network based surrogate model of...

24. Transferability and application of climate emulators to produce Antarctic ice sheet surface mass balance

Clément Cherblanc (Niels Bohr Institute + Danish Meteorological Institute)

21/08/2026, 13:25

Plenary

Regional Climate Models (RCMs) bring significant added value compared to using global earth system models directly for modelling the Surface Mass Balance of the Antarctic Ice Sheet. However the high computational cost of RCMs limits their application across both the CMIP ensemble and in running different pathways. An alternative is the use of deep learning based climate emulators to expand the...

10. Interpretable Target-Aware Deep Learning: Linking Weather Patterns to Precipitation

Mr Jonathan Ortved Melcher (University of Copenhagen, Niels Bohr Institute, Physics of Ice Climate and Earth)

21/08/2026, 13:45

Plenary

Classical dimensionality reduction has served physics well. PCA is fast, transparent, and cleanly ranks structures by explained variance, which is why it remains the default first look for almost any high-dimensional dataset. But its strengths come from assumptions the underlying physics rarely respects, the modes must be linear and mutually orthogonal, forcing genuinely distinct physical...

31. From Micro to Macro-World: Device-Agnostic Single-Emitter Super-Resolution Imaging using Deep Learning

Dominik Vašinka (Palacký University Olomouc)

21/08/2026, 14:05

Poster

Any optical imaging system, from microscopes to telescopes, is subjected to diffraction owing to its finite aperture. For objects composed of many single emitters, such as molecules, quantum dots, or stars, diffraction blurring can significantly impair our ability to determine the number and positions of emitters present. Deep learning techniques showed great potential in overcoming this...

3. Does extrinsic flux patterns support clustering by intrinsic oscillations in insect lidar?

Mikkel Brydegaard (Lund Univeristy)

21/08/2026, 14:10

Poster

What goes up doesn’t necessarily come down. No insects are born in the air but many ends their lives in bellies of predators. Over breeding sites this imbalance would be exaggerated and during invasions the flux could be reversed. Our group currently develops lidar tools for online in situ biodiversity assessment. We can detect hundreds of thousands of insects per day and differentiating...

1. Magnetic-field controlled perfect chirality in a on-chip coherent spin–photon interface

Dr Shikai Liu (The Niels Bohr Institute, University of Copenhagen)

21/08/2026, 14:15

Poster

The ability to engineer chiral spin-photon interactions is essential for exploring quantum networks and non-reciprocal quantum optics. Here, we demonstrate chiral coupling of a negatively charged quantum dot spin, i.e., a four-level system, embedded in a standard W1 photonic-crystal waveguide. Despite the absence of global chirality, we observe spin-dependent directional emission of...

44. Outlier Detection in the BedMap Dataset

Mr Janik Happel (NBI, KU)

21/08/2026, 14:20

Poster

The BedMap compilation of Antarctic ice-thickness measurements contains 74.7 million data points collected over five decades (1966–2020). Measurement noise, unphysical jumps, and the ongoing ice flow over this period mean that much of the older data is unreliable, and such corrupted measurements propagate into the climate models built on top of the dataset. We present a semi-supervised graph...