Thomas Ceulemans

Asymptotic Giant Branch (AGB) stars significantly contribute to the chemical composition of the universe. In their outflows, complex chemistry takes place, which critically depends on the local temperature. Therefore, if we want to accurately model the AGB environment, we need accurate cooling rates. The CO molecule is abundant in AGB outflows, and has a dipole moment, which enables it to cool through emission from its rotational transitions. We therefore expect it to significantly contribute to cooling in this environment, even at low temperatures (10 K T 3000 K). Currently, CO cooling rates are available for interstellar medium (ISM)-like conditions, which encompasses a different parameter regime, with generally lower densities and velocity gradients, compared to AGB winds. Therefore, these ISM cooling rates might not be applicable to the AGB regime. In this paper, we compute CO cooling rates for hydrodynamics simulations of AGB outflows. To evaluate the net cooling rate, we calculate the energy level distribution of CO self-consistently, using the non-Local Thermodynamical Equilibrium (NLTE) line radiative transfer code magritte. We verify whether already existing CO cooling rate prescriptions for the ISM are applicable for this regime. We noticed minor differences between these prescriptions and our calculated cooling rates in general. However, when used far outside their originally intended parameter regimes, significant differences occur. Therefore, we propose a new CO cooling rate prescription for the AGB environment and we study how the computed cooling rate varies depending on input parameters.

Radiative transfer is essential in astronomy, both for interpreting observations and simulating various astrophysical phenomena. However, self-consistent line radiative transfer is computationally expensive, especially in 3D. To reduce the computational cost when utilizing a discrete angular discretization, we use a comoving frame interpretation of the radiative transfer equation. The main innovation of this paper lies in the novel stabilization method for the resulting numerical discretization. The stabilization method is able to reduce spurious oscillatory behavior in the computed intensities, at the expense of extra boundary conditions which need to be enforced. We also implement an adaptive angular discretization for the ray-tracing implementation, in order to efficiently and accurately calculate the radiation field. Finally, we apply this new numerical method to compute NLTE line radiative transfer on a hydrodynamics model, showcasing its potential improvement in computation efficiency.

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.

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.