Spectuner-D1: Spectral Line Fitting of Interstellar Molecules Using Deep Reinforcement Learning

Spectral lines from interstellar molecules provide crucial insights into the physical and chemical conditions of the
interstellar medium. Traditional spectral line analysis relies heavily on manual intervention, which becomes
impractical when handling the massive datasets produced by modern facilities such as the Atacama Large
Millimeter/submillimeter Array (ALMA). To address this challenge, we introduce a novel deep reinforcement
learning framework to automate spectral line fitting. Using observational data from ALMA, we train a neural
network that maps both molecular spectroscopic data and observed spectra to physical parameters such as
excitation temperature and column density. The neural network predictions can serve as initial estimates and be
further refined using a local optimizer. Our method achieves fitting results consistent with global optimization
with multiple runs, while reducing the number of forward modeling runs by an order of magnitude. We apply our
method to pixel-level fitting for an observation of the G327.3-0.6 hot core and validate our results using XCLASS.
We perform the fitting for typical complex organic molecules of hot cores, including CH3OH, CH3OCHO,
CH3OCH3, C2H5CN, and C2H3CN. For a region of 100 × 100 pixels covering 5 GHz bandwidth, the fitting
process requires 4.9–41.9 minutes using a desktop with 16 cores and one consumer-grade GPU card.