Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning

Context. The outer Milky Way has a lower metallicity than our solar neighbourhood, but still many molecules are detected in the region. Molecular line ratios can serve as probes to better understand the chemistry and physics in these regions. Aims. We use interpretable machine learning to study 9 different molecular ratios, helping us understand the forward connection between the physics of these environments and the carbon and oxygen chemistries. Methods. Using a large grid of astrochemical models generated using UCLCHEM, we study the properties of molecular clouds of low oxygen and carbon initial abundance. We first try to understand the line ratios using a classical analysis. We then move on to using interpretable machine learning, namely Shapley Additive Explanations (SHAP), to understand the higher order dependencies of the ratios over the entire parameter grid. Lastly we use the Uniform Manifold Approximation and Projection technique (UMAP) as a reduction method to create intuitive groupings of models. Results. We find that the parameter space is well covered by the line ratios, allowing us to investigate all input parameters. SHAP analysis shows that the temperature and density are the most important features, but the carbon and oxygen abundances are important in parts of the parameter space. Lastly, we find that we can group different types of ratios using UMAP. Conclusions. We show the chosen ratios are mostly sensitive to changes in the carbon initial abundance, together with the temperature and density. Especially the CN/HCN and HNC/HCN ratio are shown to be sensitive to the initial carbon abundance, making them excellent probes for this parameter. Out of the ratios, only CS/SO shows a sensitivity to the oxygen abundance.

3D-PDR Orion dataset and NeuralPDR: Neural Differential Equations for Photodissociation Regions

We present a novel dataset of simulations of the photodissociation region (PDR) in the Orion Bar and provide benchmarks of emulators for the dataset. Numerical models of PDRs are computationally expensive since the modeling of these changing regions requires resolving the thermal balance and chemical composition along a line-of-sight into an interstellar cloud. This often makes it a bottleneck for 3D simulations of these regions. In this work, we provide a dataset of 8192 models with different initial conditions simulated with 3D-PDR. We then benchmark different architectures, focusing on Augmented Neural Ordinary Differential Equation (ANODE) based models (Code be found at https://github.com/uclchem/neuralpdr). Obtaining fast and robust emulators that can be included as preconditioners of classical codes or full emulators into 3D simulations of PDRs.

Disentangled Representation Learning for Astronomical Chemical Tagging

Modern astronomical surveys are observing spectral data for millions of stars. These spectra contain chemical information that can be used to trace the Galaxy's formation and chemical enrichment history. However, extracting the information from spectra, and making precise and accurate chemical abundance measurements are challenging. Here, we present a data-driven method for isolating the chemical factors of variation in stellar spectra from those of other parameters (i.e. \teff, \logg, \feh). This enables us to build a spectral projection for each star with these parameters removed. We do this with no ab initio knowledge of elemental abundances themselves, and hence bypass the uncertainties and systematics associated with modeling that rely on synthetic stellar spectra. To remove known non-chemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation. We simulate our recovery of chemically identical stars using the disentangled spectra in a synthetic APOGEE-like dataset. We show that this recovery declines as a function of the signal to noise ratio, but that our neural network architecture outperforms simpler modeling choices. Our work demonstrates the feasibility of data-driven abundance-free chemical tagging.