利用监督机器学习对银道面致密射电源进行分类
Classification of compact radio sources in the Galactic plane with supervised machine learning
AI总结：
本文构建约2万张银道面致密射电源多波段图像数据集，分别用梯度提升决策树和卷积神经网络进行监督分类，实现河内与河外源分离F1>90%，并公开工具与模型。
摘要：
从处理后的数据产品生成可供科学使用的数据，是下一代射电连续谱巡天（如平方公里阵列 SKA 及其前驱设施）面临的主要挑战之一，原因在于预期的数据量巨大，且需要实现高度自动化的处理。源提取、特征描述和分类是这一过程中的主要阶段。在本工作中，我们聚焦于利用射电和红外图像作为输入，对银道面中的致密射电源进行分类。为此，我们构建了一个经过整理的约 20,000 张不同天文类别致密源图像的数据集，这些图像来自过去的射电和红外巡天，以及利用澳大利亚 SKA 探路者（ASKAP）开展的先导巡天所获得的新射电数据。我们还为部分数据获取了射电谱指数信息。随后，我们在所构建的数据集上训练了两种不同的分类器。第一种模型使用梯度提升决策树，并在从数据中预先计算的一组特征上进行训练，这些特征包括射电-红外颜色指数和射电谱指数。第二种模型直接在多通道图像上进行训练，采用卷积神经网络。通过完全监督的方法，我们在将银河系内天体与河外背景分离的任务中取得了较高的分类准确率（F1 分数大于 90%）。各类别之间的区分性能在 60% 到 75% 之间，当加入远红外和谱指数信息后，性能提高了 10%，其中河外天体、行星状星云（PNe）和 HII 区能够以更高的准确率被识别。所实现的工具和训练好的模型已公开发布，并提供给射电天文界，以便未来应用于新的射电数据。
英文摘要：
Generation of science-ready data from processed data products is one of the major challenges in next-generation radio continuum surveys with the Square Kilometre Array (SKA) and its precursors, due to the expected data volume and the need to achieve a high degree of automated processing. Source extraction, characterization, and classification are the major stages involved in this process. In this work we focus on the classification of compact radio sources in the Galactic plane using both radio and infrared images as inputs. To this aim, we produced a curated dataset of ~20,000 images of compact sources of different astronomical classes, obtained from past radio and infrared surveys, and novel radio data from pilot surveys carried out with the Australian SKA Pathfinder (ASKAP). Radio spectral index information was also obtained for a subset of the data. We then trained two different classifiers on the produced dataset. The first model uses gradient-boosted decision trees and is trained on a set of pre-computed features derived from the data, which include radio-infrared colour indices and the radio spectral index. The second model is trained directly on multi-channel images, employing convolutional neural networks. Using a completely supervised procedure, we obtained a high classification accuracy (F1-score>90%) for separating Galactic objects from the extragalactic background. Individual class discrimination performances, ranging from 60% to 75%, increased by 10% when adding far-infrared and spectral index information, with extragalactic objects, PNe and HII regions identified with higher accuracies. The implemented tools and trained models were publicly released, and made available to the radioastronomical community for future application on new radio data.
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