VMC巡天：LI. 利用概率随机森林监督机器学习算法分类河外天体
The VMC Survey : LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm
AI总结：
本研究利用概率随机森林算法对VMC巡天约1.3亿个天体分类，结合多波段测光与光谱数据，在LMC、SMC天区取得高分类准确率，新发现一批AGN、星系及年轻恒星天体候选体。
摘要：
我们使用一种监督机器学习算法（概率随机森林）对VISTA麦哲伦云巡天（VMC）中约1.3亿个天体进行分类。我们采用从光学到远红外的多波段测光数据作为训练特征，训练样本包含活动星系核（AGN）、星系以及各类恒星的光谱数据，其中光谱数据来自南非大望远镜（SALT）和南非天文台（SAAO）1.9米望远镜的新观测结果。我们还为仍未识别的天体保留了“未知”标签。\n该分类器在小麦哲伦云（SMC）天区的平均准确率约为79%，在大麦哲伦云（LMC）天区约为87%。若仅选取类别概率（P$_{class}$）大于80%的56696719个天体，SMC天区的准确率可达约90%，LMC天区可达约98%。在移除被归类为“未知”的天体后，我们共对SMC的707939个天体和LMC的397899个天体完成了分类，其中包括麦哲伦云后方超过77600个河外天体。河外天体在天区中均匀分布，而麦哲伦云天体则集中在云的中心区域，且正如预期，两类天体在光学/红外色-色图和星等图中均呈现集中分布。\n我们还使用独立数据集验证了这些分类结果，发现正如预期，大多数X射线源被归类为AGN（883个中有554个），大多数射电源被归类为AGN（2694个中有1756个）或星系（2694个中有659个），其中AGN与星系的相对比例随射电流量密度变化显著。我们发现了超过49500个此前未知的AGN候选体，其中可能包含更多处于演化关键阶段的尘埃主导型AGN；还发现了超过26500个新的星系候选体以及超过2800个新的年轻恒星天体（YSO）候选体。
英文摘要：
We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features to be trained on, and spectra of Active Galactic Nuclei (AGN), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and SAAO 1.9m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ~79% (SMC) and ~87% (LMC). Restricting to the 56,696,719 sources with class probabilities (P$_{class}$) > 80% yields accuracies of ~90% (SMC) and ~98% (LMC). After removing sources classed as 'Unknown', we classify a total of 707,939 (SMC) and 397,899 (LMC) sources, including > 77,600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour-colour/magnitude diagrams as expected. We also test these classifications using independent datasets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN-galaxy proportions vary substantially with radio flux density. We have found: > 49,500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; > 26,500 new galaxy candidates and > 2800 new Young Stellar Object (YSO) candidates.
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