Untangling Gas, Dust, and Ice Astrochemistry with JWST Ice-Mapping

JWST is revolutionising Astrochemistry of star-forming regions. When JWST data are coupled with gas-phase observations on the same spatial scales, (e.g. ALMA NOEMA KMI (RAM) or archival observations of dust and astrophysics in the same regions e.g. Gaia Eucid Herschel Spitzer, we have the potential to disentangle the astrochemistry of these environments, and understand the chemical influences on star and planet formation.
However, the problem is twofold - first we have to make equisite JWST observations, reduce the data and produce high quality ice maps, second we have to interpret that data. This involves complex processing of high volumes of data currently where there are lots of possible solutions, some more feasible than others this is the kind of big data challenge that lends itself to moden computing analysis in the machine learning AI shphere.