Extending the loop: model evaluation & discordances in astrochemistry - M. Gueguen

In this talk, we present different strategies for evaluating the adequacy of a model in context of high uncertainties, all based on the idea of iterative testing. Iterative testing is a methodology that anchors the improvement of the model's accuracy in an iterative, "looping" process, whose aim is to exploit and solve discrepancies between the model’s predictions and observations.

Based on Le Quéré (2006), we consider different phases of the development of a model and how iterative testing can guide decisions regarding the development of the model  for each one of these phases. I will then illustrate the merits and pitfalls of such a methodology based on the example of the photochemical model of Titan’s atmosphere developed over the last two decades. I will focus notably on the tools that have been conceived to improve their accuracy first in the early stages of the model construction, then at the time a more solid  photochemical network was obtained but in the absence of reliable observations, and finally when the results of the Cassini-Huygens mission were released.