Project:	Astrochemistry with the Deep Space Network (JPL AO No. 15245)
Disciplines:	Astronomy/Astrophysics, Data Science
Mentor:	Dariusz Lis, (JPL), Dariusz.C.Lis@jpl.nasa.gov, Phone: (626) 497-9374
Mentor URL:	https://science.jpl.nasa.gov/people/lis/  (opens in new window)
Background:	Spectral signatures of over 240 molecules, the majority organic, have been detected in star and planet forming regions using radio telescopes. An important area of current study is understanding the role these molecules play in seeding the chemical composition of solar and extrasolar planetary systems. Recent progress has been accelerated by radio observations at low frequencies (< 45 GHz) where large, heavy molecules have many rotational transitions. Observations of gas-phase molecules using the 70-m antenna at the Canberra Complex of the Deep Space Network are highly complementary to James Webb Space Telescope MIRI spectroscopic studies of the composition of interstellar ices and of gas-phase molecules in star forming regions. Combining these different observations will aid understanding the role chemistry plays in the composition of protoplanetary regions.
Description:	Heavy molecules, including aromatics, have many spectral lines at radio wavelengths that are too weak to be detected individually. However, the spectra can be stacked to improve the sensitivity. The student will implement Markov Chain Monte Carlo (MCMC) fitting and spectral line stacking techniques and apply them to the analysis of DSN molecular spectra of star-forming regions.
References:	Loomis, R.A., et al. 2021, Nature Astronomy 5, 188 (https://ui.adsabs.harvard.edu/abs/2021NatAs...5..188L/abstract).
Student Requirements:	Python programming skills.
Location / Safety:	Project building and/or room locations: . Student will need special safety training: .
Programs:	This AO can be done under the following programs:   Program    Available To        SURF@JPL    both Caltech and non-Caltech students  Click on a program name for program info and application requirements.