在传统调查中利用 "少量学习 "识别合并

Shoulin Wei, Xiang Song, Zhijian Zhang, Bo Liang, Wei Dai, Wei Lu and Junxi Tao
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引用次数: 0

摘要

星系合并对星系的演化轨迹和宇宙结构的广阔发展有着举足轻重的影响。基于机器学习的星系合并识别所遇到的主要挑战来自于缺乏专门针对合并星系的细致标注数据集。在本文中,我们提出了一个新颖的框架,利用少量学习技术来识别遗留巡天中的星系合并。即使面对有限的标注训练样本,少量学习也能对合并星系进行有效分类。我们采用了一种深度卷积神经网络架构,该架构在星系动物园标记的数据集上进行训练,以学习基本特征并泛化到新的实例。我们的实验结果证明了我们方法的有效性,在识别星系合并方面实现了较高的准确率和精确度,只需少量标注训练样本。此外,我们还研究了各种因素(如训练样本数量和网络架构)对少量学习模型性能的影响。所提出的方法为在大规模巡天中自动识别星系合并提供了一个前景广阔的途径,有助于对星系演化和结构形成的全面研究。为了识别星系合并,我们应用我们的方法分析了暗能量光谱仪遗留成像巡天数据发布9。结果,我们公布了一个包含 648,183 个星系合并候选者的庞大星表。我们在发表本文的同时公开发布了这个星表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Mergers in the Legacy Surveys with Few-shot Learning
Galaxy mergers exert a pivotal influence on the evolutionary trajectory of galaxies and the expansive development of cosmic structures. The primary challenge encountered in machine learning–based identification of merging galaxies arises from the scarcity of meticulously labeled data sets specifically dedicated to merging galaxies. In this paper, we propose a novel framework utilizing few-shot learning techniques to identify galaxy mergers in the Legacy Surveys. Few-shot learning enables effective classification of merging galaxies even when confronted with limited labeled training samples. We employ a deep convolutional neural network architecture trained on data sets sampled from Galaxy Zoo Decals to learn essential features and generalize to new instances. Our experimental results demonstrate the efficacy of our approach, achieving high accuracy and precision in identifying galaxy mergers with few labeled training samples. Furthermore, we investigate the impact of various factors, such as the number of training samples and network architectures, on the performance of the few-shot learning model. The proposed methodology offers a promising avenue for automating the identification of galaxy mergers in large-scale surveys, facilitating the comprehensive study of galaxy evolution and structure formation. In pursuit of identifying galaxy mergers, our methodology is applied to analyze the Data Release 9 of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. As a result, we have unveiled an extensive catalog encompassing 648,183 galaxy merger candidates. We publicly release the catalog alongside this paper.
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