Bayesian Source Separation Applied to Identifying Complex Organic Molecules in Space
This work addresses the identification of complex organic molecules in space for astrophysicists, but it is incremental as it builds on existing Bayesian methods for source separation.
The paper tackles the problem of identifying numerous Polycyclic Aromatic Hydrocarbon (PAH) species in astrophysical spectra, which is a challenging source separation issue due to hundreds or thousands of potential sources and a single measured signal. It presents a Bayesian source separation technique using nested sampling and an ON/OFF mechanism to estimate the presence and contribution of each PAH species, though no concrete numerical results are provided.
Emission from a class of benzene-based molecules known as Polycyclic Aromatic Hydrocarbons (PAHs) dominates the infrared spectrum of star-forming regions. The observed emission appears to arise from the combined emission of numerous PAH species, each with its unique spectrum. Linear superposition of the PAH spectra identifies this problem as a source separation problem. It is, however, of a formidable class of source separation problems given that different PAH sources potentially number in the hundreds, even thousands, and there is only one measured spectral signal for a given astrophysical site. Fortunately, the source spectra of the PAHs are known, but the signal is also contaminated by other spectral sources. We describe our ongoing work in developing Bayesian source separation techniques relying on nested sampling in conjunction with an ON/OFF mechanism enabling simultaneous estimation of the probability that a particular PAH species is present and its contribution to the spectrum.