Quantum Field Theory (QFT) and modern Machine Learning (ML) share deep structural analogies, from path integrals and renormalization to latent spaces and marginalization. With its solid theoretical foundation, QFT offers a powerful lens to interpret global behaviors in ML that remain poorly understood. This talk explores the interplay between QFT and ML in both directions.
In QFT, the only...
In this talk, we will present recent progress on applying machine-learning techniques to improve 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...
We report on our progress in training PolarBERT, a foundation model for the IceCube Neutrino Observatory, and studying its generalization properties under domain shift induced by simulation imperfections.
The IceCube Neutrino Observatory at the South Pole consists of a cubic kilometer of Antarctic ice, instrumented with 5,160 digital optical modules. These modules collect light induced by...
Neutrino and gamma-ray observatories might have more in common than you think. The MAGIC Telescope system, comprising a pair of 17 m Imaging Atmospheric Cherenkov Telescopes (IACTs), is located at Roque de Los Muchachos Observatory in La Palma, Spain. MAGIC is designed to detect gamma rays from around 50 GeV to over 50 TeV via atmospheric air showers. Arrays of IACTs rely on a complex pipeline...
Modern machine learning is becoming more widely applied to the field of particle accelerators. One such type of application is a virtual diagnostic (VD), where one reconstructs the output of time-consuming or destructive diagnostics using machine learning methods. In this contribution we present the application of a general structure of artificial neural networks (ANN) and training procedures...
As quantum technologies advance from foundational science to commercial deployment, a robust understanding of their surrounding ecosystem—spanning policy, workforce, and industrial dynamics—is critical. In this work, we demonstrate how AI and machine learning methods can illuminate the structure and evolution of the quantum technology (QT) ecosystem. Drawing on two large-scale datasets—(1) 62...
While artificial intelligence (AI) has brought transformative benefits across numerous domains, it has also raised serious concerns about privacy and personal data management. A prominent example is the use of publicly shared images, which can be repurposed for facial recognition and potentially lead to unwanted surveillance. In response, regulations such as the General Data Protection...
The development of innovative methods for fission trigger construction addresses the challenge of recognising fission signature in very complex detector’s response functions.
The fission recognition approaches available today have intrinsic limitations.
To draw a clearer picture, the existing dedicated detectors for fission triggering present constraints regarding experimental setup...
This work explores the potential of neural networks to find the quasi-inverse of qubit channels for any values of the channel parameters while keeping the quasi-inverse as a physically realizable quantum operation. We introduce a physics-inspired loss function based on the mean of the square of the modified trace distance (MSMTD). The scaled trace distance is used to so that the neural network...
Accurate knowledge of ice volumes is essential for predicting future sea level rise, managing freshwater resources, and assessing impacts on societies, from regional to global. Efforts to better constrain ice volumes face challenges due to sparse thickness measurements, uncertainties in model input variables, and limitations in ice flow traditional model parameterizations. Glaciers currently...
Perovskite thin films are promising for optoelectronic applications such as solar cells and LEDs, but defect formation remains a major challenge. In our study, we combine high-resolution functional intensity modulation two-photon microscopy1 with AI-enhanced data analysis to gain a deeper understanding of defect-related trap states in perovskite microcrystals2,3.
Based on methylammonium lead...
Spectroscopic characterization of quantum and low-dimensional materials remains a fundamental challenge in condensed matter physics, especially when data are noisy or scarce. In this work, we explore a general deep learning framework for the automated classification and structural identification of two-dimensional (2D) materials from Raman spectra. Our approach requires no manual feature...
While spatial resolution in flourescence microscopy and related fields during the last two decades reached the nanometer scale, the time resolution has remained essentially unchanged and is set by the camera system's imaging time. Yet adequate time resolution is crucial for accurate information acquisition about, for instance, dynamical processes in cells.
In a reaction-difffusion process...
Peatlands are carbon-rich landscapes that contain one-third of the global soil carbon (C) pool. Their capability to either sequester or release C into the atmosphere depending on the management practice, renders them important for climate action. Climate change mitigation efforts such as the rewetting of drained peatland areas are among key strategies to reduce CO2 emissions globally. In...
