Mixed membership models for source separation of spectral lines

We propose a new method for spectra modeling, that uses Splatalogue (Spectral Lines catalog) as a training data set to learn species and transitions in data cubes captured by observatories. Our model is based on Latent Dirichlet Allocation, a probabilistic generative model that is capable to capture the co occurrence of emission lines in different channels. We use Splatalogue to create a channel vocabulary, processing each species as a document in the topic model domain. The model comprises a collection of species/transitions in a comprehensive collection of channel-energy pairs. Then, we extend the model using Labeled Latent Dirichlet Allocation, exploring the capabilities of our approach to label lines in an unsupervised fashion. To the best of our knowledge, this is the first time that a probabilistic generative model is used to label spectra in Astronomy. The main advantage of our proposal is the ability to model sparse, high dimensional data using posterior inference to label new unseen data. Our Splatalogue-based mixed membership model comprises the human knowledge acquired by astronomers for decades labeling spectral lines. Experimental results show that our proposal is feasible.