arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2303.00489astro-ph.GAastro-ph.CO
收藏

miniJPAS巡天类星体选择II:具有光度测量和不确定性的机器学习分类

The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties

Natália V. N. Rodrigues, L. Raul Abramo, Carolina Queiroz, Ginés Martínez-Solaeche, Ignasi Pérez-Ràfols, Silvia Bonoli, Jonás Chaves-Montero, Matthew M. Pieri, … 展开作者

Natália V. N. Rodrigues, L. Raul Abramo, Carolina Queiroz, Ginés Martínez-Solaeche, Ignasi Pérez-Ràfols, Silvia Bonoli, Jonás Chaves-Montero, Matthew M. Pieri, Rosa M. González Delgado, Sean S. Morrison, Valerio Marra, Isabel Márquez, A. Hernán-Caballero, L. A. Díaz-García, Narciso Benítez, A. Javier Cenarro, Renato A. Dupke, Alessandro Ederoclite, Carlos López-Sanjuan, Antonio Marín-Franch, Claudia Mendes de Oliveira, Mariano Moles, Laerte Sodré, Jesús Varela, Héctor Vázquez Ramió, Keith Taylor

摘要:

天体物理学调查在很大程度上依赖于多波段测光对恒星、星系或类星体等源的分类。在窄带滤波器中的测量允许更大的区分能力,但是不同类型的物体和红移的多样性对标准的基于模板的方法提出了挑战。在这项工作中,这是旨在从miniJPAS调查中建立类星体目录的更大努力的一部分,我们提出了一种基于机器学习的方法,该方法采用卷积神经网络(CNN)来分类点状源,包括测量误差中的信息。我们使用miniJPAS调查的数据验证我们的方法,这是J-PAS合作的概念验证项目,使用J-PAS调查的56个窄带滤波器覆盖北部天空的$\sim$ 1度$^2$。由于真实数据的稀缺性,我们使用专门构建的模拟来训练我们的算法,以再现我们期望在miniJPAS调查中找到的不同类型对象的分布,以及真实观测的信号和噪声属性。我们将CNN的性能与其他基于决策树的成熟机器学习分类方法进行了比较,发现当提供测量误差作为输入时,CNN可以提高分类。在miniJPAS中预测的物体分布与恒星、类星体和未解析星系的假定光度函数一致。我们的研究结果是一个概念验证的想法,J-PAS调查将能够检测到前所未有的数量的类星体,具有很高的信心。

英文摘要:

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a Machine Learning-based method that employs Convolutional Neural Networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering $\sim$ 1 deg$^2$ of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established Machine Learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars and unresolved galaxies. Our results are a proof-of-concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑