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.
People |
ORCID iD |
| Johannes Nasim Friedrich Heyl (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/P006736/1 | 30/09/2017 | 30/03/2026 | |||
| 2322240 | Studentship | ST/P006736/1 | 30/09/2019 | 29/09/2023 | Johannes Nasim Friedrich Heyl |