arXiv subjects

Lynge Lauritsen

Publications and source records attributed to Lynge Lauritsen.

3 recordsLinked to original sources

arXiv2026

A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control

This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital Twin is intended to support the development and rigorous human-in-the-loop evaluation of AI agents for Air Traffic Control (ATC), providing a virtual representation of real-world airspace that enables safe exploration of higher levels of ATC automation. This paper makes three significant contributions: firstly, we demonstrate how historical and live operational data may be combined with a probabilistic, physics-informed machine learning model of aircraft performance to reproduce real-world traffic scenarios, while accurately reflecting the level of uncertainty inherent in ATC. Secondly, we develop a structured assurance case, following the Trustworthy and Ethical Assurance framework, to provide quantitative evidence for the Digital Twin's accuracy and fidelity. This is crucial to building trust in this novel technology within this safety-critical domain. Thirdly, we describe how the Digital Twin forms a unified environment for agent testing and evaluation. This includes fast-time execution (up to x200 real-time), a standardised Python-based ``gym'' interface that supports a range of AI agent designs, and a suite of quantitative metrics for assessing performance. Crucially, the framework facilitates competency-based assessment of AI agents by qualified Air Traffic Control Officers through a Human Machine Interface. We also outline further applications and future extensions of the Digital Twin architecture.

cs.CE↗

arXiv2025

Super-resolving Herschel - a deep learning based deconvolution and denoising technique

Dusty star-forming galaxies (DSFGs) dominate the far-infrared and sub-millimetre number counts, but single-dish surveys suffer from poor angular resolution, complicating mult-wavelength counterpart identification. Prior-driven deblending techniques require extensive fine-tuning and struggle to process large fields. This work aims to develop a fast, reliable deep-learning based deconvolution and denoising super-resolution (SR) technique. We employ a transformer neural network to improve the resolution of Herschel/SPIRE 500 $μ$m observations by a factor 4.5, using Spitzer/MIPS 24$μ$m and Herschel/SPIRE 250, 350, 500$μ$m images. Trained on SIDES and SHARK simulations, we injected instrumental noise into the input simulated images, while keeping the target images noise-free to enhance de-noising capabilities of our method. We evaluated the performance on simulated test sets and real JCMT/SCUBA-2 450 $μ$m observations in the COSMOS field which have superior resolution compared to Herschel. Our SR method achieves an inference time of $1s/deg^2$ on consumer GPUs, much faster than traditional deblending techniques. Using the simulation test sets, we show that fluxes of the extracted sources from the super-resolved image are accurate to within 5% for sources with an intrinsic flux $\gtrsim$ 8 mJy, which is a substantial improvement compared to blind extraction on the native images. Astrometric error is low ($\lesssim$ 1" vs 12" pixel scale). Reliability is $\gtrsim$ 90% for sources $>$3 mJy and $>$90% of sources with intrinsic fluxes $\gtrsim5$ mJy are recovered. Applied to real 500 $μ$m observations, fluxes of the extracted sources from the super-resolved map agree well with SCUBA-2 measured fluxes for sources $\geq$10 mJy. Our technique enables SR over hundreds of $deg^2$ without the need for fine-tuning, facilitating statistical analysis of DSFGs.

astro-ph.GA↗

arXiv2021

Super-resolving Herschel imaging: a proof of concept using Deep Neural Networks

Wide-field sub-millimetre surveys have driven many major advances in galaxy evolution in the past decade, but without extensive follow-up observations the coarse angular resolution of these surveys limits the science exploitation. This has driven the development of various analytical deconvolution methods. In the last half a decade Generative Adversarial Networks have been used to attempt deconvolutions on optical data. Here we present an autoencoder with a novel loss function to overcome this problem in the sub-millimeter wavelength range. This approach is successfully demonstrated on Herschel SPIRE 500$μ$m COSMOS data, with the super-resolving target being the JCMT SCUBA-2 450$μ$m observations of the same field. We reproduce the JCMT SCUBA-2 images with high fidelity using this autoencoder. This is quantified through the point source fluxes and positions, the completeness and the purity.

astro-ph.GA↗