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19/08/2026, 09:15
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19/08/2026, 09:30
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Dr Matthias Wilhelm (NBI)19/08/2026, 10:50
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...
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Nils Hermansson-Truedsson (University of Edinburgh)19/08/2026, 11:10
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...
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Jack Harrison (IFAE - Barcelona)19/08/2026, 11:30
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...
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Gage DeZoort (Princeton University)19/08/2026, 11:50
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...
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Lukas Liland (TU Dortmund)19/08/2026, 12:10
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)...
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Sten Åstrand (Lund University)19/08/2026, 12:15
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...
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Johann Ioannou-Nikolaides (NBI)19/08/2026, 12:20
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...
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19/08/2026, 13:25
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Prof. YAPING QI (Tohoku University)19/08/2026, 14:45
Recent advances in large language models (LLMs), foundation models, and AI agents are transforming materials science from AI-assisted characterization toward autonomous scientific discovery. While machine learning has achieved remarkable success in materials property prediction, discovering entirely new functional materials remains challenging because of the enormous chemical design space and...
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Dr Fredrik Grote (Department of physics, Stockholm University)19/08/2026, 15:05
Water is a key-ingredient for life as we know it and plays a central role in maintaining Earth’s climate within the limits that life can tolerate. Water is all around us, yet its properties are radically different from essentially all other substances. The density maximum at 4 degrees, that makes ice cubes float in our drinking glasses, is the most famous example among water’s anomalous...
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Maryna Pankratova (Uppsala University)19/08/2026, 15:25
Machine learning methods have proven useful for addressing a wide range of tasks in science and technology. Recent applications in materials science include materials discovery, predicting material properties, such as the Curie temperature, constructing phase diagrams, accelerating first-principles calculations, and more. One of the challenges in magnetism theory is accurately determining the...
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Ms Amalie Falkenberg Davidsen, Ms Frida Birkedal Nielsen, Mr Hugo Schreckenberg (University of Copenhagen), Rasmus Madsen (University of Copenhagen), Silas Billeschou Schack (University Of Copenhagen)19/08/2026, 15:45
Room temperature Superconductors are one of the most exciting materials in condensed matter physics, but the hunt for such a material still remains ongoing. The physics of known high temperature superconductors are not well understood and the mechanisms vary and depends on chemical composition and crystal structure. To circumvent the problem of understanding the physics, we instead make use of...
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Atefe Nayamadi Mahmoodabadi (Uppsala)19/08/2026, 15:50
High-Throughput Discovery of Magnetic 2D Materials:
From Physics-Informed Machine Learning to Correlated Electron SystemsThe discovery of magnetic two-dimensional (2D) materials has created new opportunities for spintronics, quantum technologies, and low-dimensional correlated electron physics. However, the enormous chemical and structural search space of magnetic monolayers makes...
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Julien Pinske (University of Rostock)19/08/2026, 15:55
The wave function encodes our knowledge of a quantum system prior to a measurement. By incorporating posterior information, one can retrospectively improve the estimate of unknown outcomes of earlier measurements. For a continuously monitored system, this inference problem is described by a Hidden Markov model whose forward and backward dynamics are governed by stochastic master equations. As...
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Mr Ali Hussein (Niels Bohr Institute University of Copenhagen), Niklas Sørensen (Københavns Universitet)19/08/2026, 16:00
Small-angle X-ray scattering (SAXS) is widely used to determine the structure of nanoparticles and biomolecules in solution. A central challenge is reconstructing the pair distance distribution function, $p(r)$, from the measured scattering intensity, $I(q)$. The $p(r)$ function describes the distribution of distances between scattering centers within a particle and provides valuable...
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20/08/2026, 09:15
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Shreya Saha (Adelaide University)20/08/2026, 10:30
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...
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Michał Kossakowski (DTU Space), Rikke Stougaard Klausen (DTU Space)20/08/2026, 10:50
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...
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Martin Ravn20/08/2026, 11:10
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...
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Miroslav Ježek (Palacký University Olomouc)20/08/2026, 11:30
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...
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Dr Bo Cao (Department of Physics and Astronomy; Nuclear Physics, Uppsala University)20/08/2026, 11:50
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...
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Stefan Cretu (Lund University)20/08/2026, 11:55
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...
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Ida Storehaug20/08/2026, 12:00
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...
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olja dordic (university of banjaluka)20/08/2026, 12:05
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...
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21/08/2026, 09:10
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Ivan Nikolic (Dawn Center, University of Copenhagen)21/08/2026, 10:30
The astrophysics of the first galaxies is undergoing a data revolution, driven by the infrared capabilities of JWST and the coming arrival of Roman, SKA, and other facilities. Yet inferring the physical processes governing the formation and evolution of the earliest galaxies remains difficult: these objects are distant and intrinsically faint, and several well-established scaling relations...
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Dr Danial Langeroodi (University of Cambridge)21/08/2026, 10:50
The chemical enrichment state of galaxies is a result of a complex interplay between several mechanisms that (i) accrete pristine gas onto galaxies, (ii) cool down, fragment, and convert this pristine gas into stars, (iii) produce the heavy elements in the cores of massive stars, and (iv) release the newly formed heavy elements into the interstellar medium through stellar winds and supernovae....
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Dr Sinan Deger (University of Cambridge)21/08/2026, 11:10
Panchromatic surveys such as COSMOS have expanded our understanding of how galaxies evolve through cosmic time immensely. Surveys such as the Vera C. Rubin Observatory's LSST are promising an unprecedented view of this evolutionary paradigm provided the massive data challenge they pose is answered. We have been developing pop-cosmos, a comprehensive galaxy population model housing a...
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Prof. Aaron Golden (University of Galway)21/08/2026, 11:30
Under the assumption that other advanced extraterrestrial civilisations exist - or have existed - elsewhere in our and other galaxies, the Breakthrough Listen Initiative has for the past decade taken the lead in the search for both direct and indirect technosignatures using a diversity of radio telescope facilities. Advances in instrumentation, computational processing power and most...
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Adriana Dropulic (Niels Bohr Institute)21/08/2026, 11:50
Stellar streams, the remnants of tidally disrupted dwarf galaxies and globular clusters, are powerful probes of galaxy assembly and dark matter. While many streams have been discovered in the Milky Way, comparisons with cosmological simulations suggest that both the census of streams and the identification of their constituent stars remain incomplete due to overwhelming stellar backgrounds,...
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Mr Davey Plugers (University of Copenhagen), Zhen Xiang (University of Edinburgh)21/08/2026, 11:55
The UV continuum slope (β) of high-redshift galaxies is an important observable for understanding the stellar populations, dust attenuation, and ionizing photon production of galaxies during the Epoch of Reionization. However, the relationship between UV β slopes and underlying galaxy properties is highly non-linear, involving the combined effects of star formation, metallicity, gas evolution,...
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Nikki Arendse (University of Nova Gorica)21/08/2026, 12:00
When a supernova is located behind a massive galaxy, its light can be gravitationally lensed to form multiple images. Such a strongly lensed supernova is a rare and powerful probe that provides insights into distant supernova explosions, dark matter in galaxies, and even the expansion rate of the Universe. Currently, the lensed supernova field stands at a turning point, as we transition from a...
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Qi-fan Wu21/08/2026, 13:05
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...
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Clément Cherblanc (Niels Bohr Institute + Danish Meteorological Institute)21/08/2026, 13:25
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...
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Mr Jonathan Ortved Melcher (University of Copenhagen, Niels Bohr Institute, Physics of Ice Climate and Earth)21/08/2026, 13:45
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...
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Dominik Vašinka (Palacký University Olomouc)21/08/2026, 14:05
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...
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Mikkel Brydegaard (Lund Univeristy)21/08/2026, 14:10
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...
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Dr Shikai Liu (The Niels Bohr Institute, University of Copenhagen)21/08/2026, 14:15
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...
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Mr Janik Happel (NBI, KU)21/08/2026, 14:20
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...
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21/08/2026, 14:25
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21/08/2026, 15:15
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