Before starting my Phd, I obtained Bachelor’s degrees in Mathematics and Physics and a Master’s in Mathematics at KU Leuven.
My research interests lie in the field of applied numerical mathematics, in particular on how to compute radiative transfer efficiently. I am currently contributing to the development of the open-source radiative transfer library Magritte.
Spectral line observations are an indispensable tool to remotely probe the physical and chemical conditions throughout the universe. Modelling their behaviour is a computational challenge that requires dedicated software. In this paper, we present the first long-term stable release of Magritte, an open-source software library for line radiative transfer. First, we establish its necessity with two applications. Then, we introduce the overall design strategy and the application/programmer interface (API). Finally, we present three key improvements over previous versions: (1) an improved re-meshing algorithm to efficiently coarsen the spatial discretisation of a model; (2) a variation on Ng-acceleration, a popular acceleration-of-convergence method for non-LTE line transfer; and, (3) a semi-analytic approximation for line optical depths in the presence of large velocity gradients.
@article{ceulemans2024magrittetransfer,author={Ceulemans, T and De Ceuster, F and Decin, L and Yates, J},journal={ASTRONOMY AND COMPUTING},month=oct,number={ARTN 100889},publisher={ELSEVIER},title={MAGRITTE, a modern software library for spectral line radiative transfer},volume={49},year={2024},doi={10.1016/j.ascom.2024.100889},issn={2213-1337},eissn={2213-1345},keyword={CONVERGENCE},language={English},publicationstatus={published},}
ApJS
Bayesian Model Reconstruction Based on Spectral Line Observations
F De Ceuster, T Ceulemans, L Decin, and 2 more authors
Spectral line observations encode a wealth of information. A key challenge, therefore, lies in the interpretation of these observations in terms of models to derive the physical and chemical properties of the astronomical environments from which they arise. In this paper, we present pomme, an open-source Python package that allows users to retrieve 1D or 3D models of physical properties, such as chemical abundance, velocity, and temperature distributions of (optically thin) astrophysical media, based on spectral line observations. We discuss how prior knowledge, for instance, in the form of a steady-state hydrodynamics model, can be used to guide the retrieval process, and we demonstrate our methods on both synthetic and real observations of cool stellar winds.
@article{deceuster2024bayesianobservations,author={De Ceuster, F and Ceulemans, T and Decin, L and Danilovich, T and Yates, J},journal={Astrophysical Journal Supplement Series},month=dec,number={2},publisher={IOP Publishing},title={Bayesian Model Reconstruction Based on Spectral Line Observations},volume={275},year={2024},doi={10.3847/1538-4365/ad89a2},issn={0067-0049},eissn={1538-4365},day={5},publicationstatus={published},}
