A Lightweight Deep Learning Framework for Galaxy Morphology Classification

With the construction of large telescopes and the explosive growth of observed galaxy data,we are facing the problem to improve the data processing efficiency while ensuring the accuracy of galaxy morphology classification.Therefore,this work designed a lightweight deep learning framework,Efficient Net-G3,for galaxy morphology classification.The proposed framework is based on Efficient Net which integrates the Efficient Neural Architecture Search algorithm.Its performance is assessed with the data set from the Galaxy Zoo Challenge Project on Kaggle.Compared with several typical neural networks and deep learning frameworks in galaxy morphology classification,the proposed Efficient Net-G3 model improved the classification accuracy from 95.8% to 96.63% with F1-Score values of 97.1%.Typically,this model uses the least number of parameters,which is about one tenth that of DenseNet161 and one fifth that of ResNet-26,but its accuracy is about one percent higher than them.The proposed Efficient Net-G3 can act as an important reference for fast morphological classification for massive galaxy data in terms of efficiency and accuracy.

The Initial Case of COVID-19—Shulan City,Jilin Province,China,May 8,2020

Coronavirus disease 2019(COVID-19)was designated a global pandemic by the World Health Organization(WHO)on March 11,2020(1).After great effort,COVID-19 has been well-controlled in China,but new challenges have emerged due to increasing numbers of imported cases from outside of China.On May 8,the Health Commission of Jilin Province reported a confirmed COVID-19 case of a 45-year-old laundry woman from Shulan City in the northeast of China.This case was suspected to be associated with a possible importation event.