Application of Artificial Neural Networks on Stellar Classification Based on Multi-band Photometry

• Main Author: 鍾明瑾
• Other Authors: Hung, Shih-Lin
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/3a5r7e

碩士 === 國立交通大學 === 土木工程系所 === 106 === Stellar classification is a fundamental work in astronomy. Since the classifier used today is overwhelmed by the rapidly growth of data set, new methods should be developed. In this study, A stellar classification is proposed, consists of several neural network models built by open source library Tensorflow, which is well-known by its visualize ability and supportive of computing ability to one or more CPUs or GPUs. 50043 stars are classified into Morgan–Keenan (MK) system using multi-band photometry extract from Sloan Digital Sky Survey (SDSS), UKIDSS, and WISE. The result demonstrates the feasibility of using multi-band photometry as input by comparing different setting of parameters and revealing the performance of every classifier. Models can reach 98% and 93% accuracy while classifying M type and K type and it's ready to serve online.