报告题目：Inverting cosmic ray propagation by Convolutional Neural Networks

　　报告人简介：蔡岳霖，中国台湾新竹清华大学研究学者。2011年博士毕业于University of Sheffield，先后在波兰核子研究所、日本东京大学IPMU、中国台湾新竹清华大学以及中国台湾”中央研究院“从事研究工作。2019-2020受中国科学院台湾青年学者计划资助在紫金山天文台从事合作研究。研究方向为暗物质相关的粒子物理、宇宙学和天文学，发表40余篇研究论文，共计被引用超过1700次，有数项工作被国际权威的《粒子物理手册》等收录。

　　摘要：We propose a machine learning method to investigate the propagation of cosmic rays, based on the precisely measured spectra of primary and secondary nuclei Li, Be, B, C, and O by AMS-02, ACE, and Voyager-1. We train two Convolutional Neural Network machines: one learns how to invert the spectra of cosmic rays to the propagation and source parameters, and the other one is similar to the former but with an additional denoising. Together with the mock data generating by GALPROP, we found that both machines can properly invert the propagation process and infer the propagation and source parameters reasonably well. This approach can be much more efficient than the traditional Markov Chain Monte Carlo fitting method in deriving the propagation parameters.

　　报告时间：9月16日上午10:30

　　报告方式：腾讯会议5032471346

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