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

使用贝叶斯统计和机器学习来了解颗粒表面化学

• 批准号: 2322240
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2322240
• 关键词: Using; Bayesian; Statistics; Machine; Learning

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.

有大量的不确定性围绕粮食表面化学过程，尽管这些被推测为是至关重要的形成更复杂的分子，如氨基酸甘氨酸。在这个项目中，我将使用贝叶斯统计和机器学习技术来提高我们对这些过程的理解，并预测星际尘埃颗粒上可能形成的前生物物种。