Extracting More Chemistry from Spectral Surveys

Many molecules contribute signals too weak to interpret one transition at a time. Spectral stacking combines their expected lines, and matched filtering tests the resulting pattern against a model. We use these methods together with Bayesian fitting to evaluate detections and constrain molecular abundances, taking account of line blending and the physical conditions of the source.

Our work on aggregate isotope signals tests how these techniques can probe carbon-isotope ratios when individual rare isotopic species are too faint to detect. The published proof of concept establishes how these searches can test aromatic formation histories. We are pairing this approach with measurements of the required laboratory spectra. In parallel, our automated survey-assignment methods combine spectroscopic matches with chemical context to analyze dense line surveys and suggest targets for follow-up.