Using Bayesian Statistics and Machine Learning to understand grain-surface chemistry

Lead Research Organisation: University College London
Department Name: Physics and Astronomy

Abstract

There is a significant amount of uncertainty surrounding grain-surface chemistry processes, despite these being speculated as being crucial to the formation of more complex molecules, such as the amino acid glycine. In this project, I will look to use Bayesian statistics and machine learning techniques to improve our understanding of these process as well as make predictions about what pre-biotic species could potentially form on interstellar dust grains.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/P006736/1 01/10/2017 30/09/2024
2322240 Studentship ST/P006736/1 01/10/2019 30/09/2023 Johannes Nasim Friedrich Heyl