Oral Abstract

Invited talk (I0.2) Serena Viti ()

Statistical and machine learning approach to the study of astrochemistry.

In order to draw the real potential of molecules as tracers of the dense gas in the ISM,
accurate estimates of the abundances of molecular
species as a function of all the physical parameters that influence their chemistry must be obtained.
Coupling chemical and radiative transfer models for the interpretation of molecular emission
has been successful in achieving the above to different degrees. However understanding the physical conditions in the molecular gas in the ISM is an inverse problem subject to complicated chemistry that varies non-linearly with both time and the physical environment. In this talk I will present examples of a new approach for the
interpretation of astrochemical modelling using Bayesian and Machine Learning techniques.