A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS
A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS
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识别亚毫米星系多波长对应物的机器学习方法：使用 AS2UDS 和 ALESS 进行训练和测试
DOI：
10.3847/1538-4357/aacdaa
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发表时间：
2018-06
影响因子：
4.9
通讯作者：
Conselice C
中科院分区：
文献类型：
--
作者：
An Fang Xia;Stach S M;Smail Ian;Swinbank A M;Almaini O;Simpson C;Hartley W;Maltby D T;Ivison R J;Arumugam V;Wardlow J L;Cooke E A;Gullberg B;Thomson A P;Chen Chian Chou;Simpson J M;Geach J E;Scott D;Dunlop J S;Farrah D;van der Werf P;Blain A W;Conselice C
我们描述了有监督的机器学习算法的应用，以识别在全景、单盘亚毫米测量中检测到的亚毫米源的可能的多波长对应物。作为训练集，我们使用了695个(S870μm≳1 mJy)亚毫米星系(SMG)的样本，这些SMG的精确识别来自ALMA对Scuba-2宇宙学遗产调查的UKIDSS-UDS场(AS2UDS)的后续行动。我们发现射电辐射、近/中红外颜色、光度红移和绝对H波段星等是区分SMGs和亚毫米暗场星系的有效预报因子。我们的无线电+机器学习相结合的方法能够成功地恢复∼85%的ALMA识别的SMG，这些SMG至少在三个波段从紫外线到无线电被探测到。我们将训练集划分为独立的子集，分别用于训练和测试，并将我们的方法应用于来自∼/LABOCA ECDF-South Survey(ALESS)的ALMA识别的SMG的独立样本，以确认我们方法的稳健性。为了进一步测试我们的方法，我们将870MALMA图堆叠在K波段星系的位置上，这些星系被机器学习归类为SMG对应星系，但没有>；4.3μALMA探测。这些星系的中值峰值通量密度为S870mJy=(0.61±0.03)μ，表明我们的方法可以恢复微弱和/或弥散的SMG，即使它们低于我们ALMA观测的探测阈值。未来，我们将把这种方法应用于从全景单盘亚毫米测量中提取的样本，这些样本目前缺乏干涉跟踪观测，以解决只能通过SMG的大统计样本来解决的科学问题。
We describe the application of supervised machine-learning algorithms to identify the likely multiwavelength counterparts to submillimeter sources detected in panoramic, single-dish submillimeter surveys. As a training set, we employ a sample of 695 (S870μm ≳ 1 mJy) submillimeter galaxies (SMGs) with precise identifications from the ALMA follow-up of the SCUBA-2 Cosmology Legacy Survey’s UKIDSS-UDS field (AS2UDS). We show that radio emission, near-/mid-infrared colors, photometric redshift, and absolute H-band magnitude are effective predictors that can distinguish SMGs from submillimeter-faint field galaxies. Our combined radio + machine-learning method is able to successfully recover ∼85% of ALMA-identified SMGs that are detected in at least three bands from the ultraviolet to radio. We confirm the robustness of our method by dividing our training set into independent subsets and using these for training and testing, respectively, as well as applying our method to an independent sample of ∼100 ALMA-identified SMGs from the ALMA/LABOCA ECDF-South Survey (ALESS). To further test our methodology, we stack the 870 μm ALMA maps at the positions of those K-band galaxies that are classified as SMG counterparts by the machine learning but do not have a >4.3σ ALMA detection. The median peak flux density of these galaxies is S870μm = (0.61 ± 0.03) mJy, demonstrating that our method can recover faint and/or diffuse SMGs even when they are below the detection threshold of our ALMA observations. In future, we will apply this method to samples drawn from panoramic single-dish submillimeter surveys that currently lack interferometric follow-up observations to address science questions that can only be tackled with large statistical samples of SMGs.
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DOI：
10.1111/j.1365-2966.2007.12040.x
发表时间：
2006-04
影响因子：
4.8
作者：
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2012-03
影响因子：
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DOI：
10.1086/428082
发表时间：
2004-12
期刊：
The Astrophysical Journal
影响因子：
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影响因子：
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DOI：
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2000-03
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影响因子：
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