Subjects: Astronomy submitted time 2024-04-12 Cooperative journals: 《天文学报》
Abstract： The characteristics of Low Surface Brightness Galaxies (LSBGs) are very important for understanding the overall characteristics of galaxies. It is of great significance to search and expand the samples of low surface brightness galaxies by modern machine learning, especially deep learning algorithm. LSBGs are difficult to discern automatically and accurately with traditional methods because of their obscure features. However, deep learning does have the advantage of automatically identifying complex and effective features. To solve this problem, an algorithm named You Only Look Once version X-CS (YOLOX-CS) is proposed to search LSBG in large sample sky survey. Firstly, five classical target detection algorithms are compared through experiments and the optimal YOLOX algorithm is selected as the basic algorithm. Then, the YOLOX-CS framework is constructed by combining different attention mechanisms and different optimizers. The data set uses images from the Sloan Digital Sky Survey (SDSS), labelled from LSBG in the $\alpha$.40-SDSS DR7 (the cross coverage area of 40\% HI Arecibo Legacy Fast ALFA Survey and SDSS Data Release7) survey. Due to the small number of samples in this data set, Deep Convolutional Generative Adversarial Networks (DCGAN) model is used to expand the experimental test data. After comparing with a series of target detection algorithms, YOLOX-CS has a good test result in searching LSBG recall rate and Average Precision (AP) value in two data sets before and after expansion. The recall rate and AP value in the test set without expansion data set reach 97.75\% and 97.83\%, respectively. In the expanded data set of DCGAN model, under the same test set, the recall rate reaches 99.10\% and the AP value reaches 98.94\%, which proves that the algorithm has excellent performance in LSBG search. Finally, the algorithm is applied to SDSS photometric data, and 765 LSBG candidates are obtained.
Subjects: Astronomy submitted time 2024-04-12 Cooperative journals: 《天文学报》
Abstract： The morphology of galaxies is closely related to the formation and evolution of galaxies, and its morphological classification is a significant part of the follow-up research of galaxy astronomy. With the emergence of massive astronomical observation data, the automatic analysis of astronomical data has attracted more and more attention. To solve this problem, the advanced deep learning backbone network EfficientNetV2 is utilized to analyze the effects of different attention mechanism types and usage nodes on network performance, and an improved algorithm model named EfficientNetV2-S-Triplet7 is constructed to realize automatic classification of galaxy morphology. More than 240 thousand photometric images from Galaxy Zoo 2 are used as initial data for experimental tests. In the process of data preprocessing, image enhancement methods such as size jittering, flipping and color distortion are adopted to solve the problem of image number imbalance. After conducting comparative experiments on the same series of classic and cutting-edge deep learning algorithms AlexNet, RegNet, MobileNetV2 and ResNet-34, it is concluded that the EfficientNetV2-S-Triplet7 algorithm has the best test results in classification accuracy, recall and F1-score. In 9375 test images, the three index values can reach 89.03\%, 90.21\% and 89.93\%, respectively, and the precision can reach 89.69\%, ranking the third among other models. The results show that EfficientNetV2-S-Triplet7 algorithm can be effectively applied to the morphological classification of large-scale galaxy data.
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