We present a local framework for investigating non-unitary evolution groups pertinent to effective field theories in general semi-classical spacetimes. It is based on a rigorous local stability analysis of the algebra of observables and solely employs geometric concepts in the functional representation of quantum field theory. In this representation, it is possible to construct infinitely many...
The search for Earth-like planets around Sun-like stars using Doppler radial velocity (RV) measurements is challenged by stellar variability. Stellar variability alters the shapes of spectral lines, introducing spurious RVs that obscure the true RV signals of planets.
To address this issue, we study the Sun as a star, where we know the ground truth of the solar RVs as a result of solar...
Recently proposed Neural-LAM forecasting models (https://arxiv.org/abs/2309.17370, https://arxiv.org/abs/2406.04759) bring state-of-the-art graph-based approaches to Limited Area Modeling (LAM). The models generate and use an area-restricted graph and take forcing inputs from a host model to handle the boundary conditions. Initial promising results have motivated interest in developing the...
Two-dimensional (2D) materials are gaining significant attention for their unique properties and potential applications. Raman spectroscopy is a rapid, non-destructive tool for characterizing these materials, but traditional analysis methods are often time-consuming and subjective. In this study, we leverage deep learning, including classificatory and generative models, to enhance Raman...
Information processing and analysis of time series are crucial in many applications but often face constraints such as high computational complexity. Quantum reservoir computing, which combines a reservoir of neuromorphic quantum hardware with a simple neural network, offers a promising solution. By utilizing the high-dimensional space and dynamics of quantum systems, this approach enables the...
Geomagnetic storms, resulting from solar activity, pose significant risks to satellite operations, communication systems, and power grids. Accurate forecasting of these storms is crucial for mitigating their impacts. As the final project in the course "Applied Machine Learning" at the University of Copenhagen, we explore the application of convolutional neural networks (CNNs) and recurrent...
The James Webb Space Telescope is helping astronomers to push back the frontiers of observability. This is particularly true for exoplanets, as we are now looking at small, terrestrial planets, searching for hints of an atmosphere. These signals are very small, and as with every new instrument, most systematics (and their source) remain very poorly understood. In an effort to investigate...
Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern many problems ranging from weather and climate prediction to turbulence simulations. Recent advances have seen machine learning (ML) increasingly...
FoCal-H is a hadronic calorimeter currently under development and is intended as a part of an upgrade to the ALICE experiment at the LHC at CERN. As part of an Applied Machine Learning course, we looked at real data from FoCal-H and used machine learning techniques on it. Data was collected during testbeams where particle type and energy are selected and fired at the device. With labelled data...
The advent of gravitational wave astronomy has enabled the direct study of the synthesis of heavy elements such as rare earths, lanthanides, and actinides. This was exemplified by the kilonova AT2017gfo/GW170817, where several heavy element emission lines were confirmed. The primary challenge in advancing this research is the lack of accurate and complete atomic data for these elements in...
Our understanding of past climate and the mechanisms that drive climate change can be improved through analysis of the excellent palaeoclimatic data available in the Greenland ice cores, but such insights depend on having an established chronology of the ice core. Therefore, the dating of ice cores is an essential part of palaeoclimatic science. However, this dating is often carried out by...
Extended theories of gravity motivated by large-scale cosmological observations as well as quantum structure at black hole horizons, which has been proposed as a solution to Hawking’s information paradox, may produce detectable signatures in gravitational waves (GW) emitted by compact binary mergers. The growing number of events reported by the increasingly sensitive network of earth-based GW...
This study aims to determine glacier thickness based on observational data. Due to global warming the rising temperatures leads to increased melt from land-based bodies of ice. Increased melt from glaciers both pose a risk to societies that are based along rivers produced from glacier discharge, as well as having direct impact on sea level rise. Determining the thickness or the mass of these...
Star formation is a multi-scale problem. Theoretically, only global simulations that account for the connection from the large-scale gas flow to the accreting protostar reflect the observed complexity of protostellar systems. In such global models, star are born in a stochastic process as a statistical ensemble and it is not possible to create constrained models that match specific...
