论文标题
对机器学习的光谱观察结果从Allwise Sky调查中选出的异常目录
Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey
论文作者
论文摘要
我们介绍了一个程序的结果，以搜索和识别全套宽阔的红外红外调查Explorer（WISE），该探索器（WISE）基于用于异常检测的机器学习算法，即单级支持矢量机（OCSVM）。该算法旨在检测偏离由已知类别组成的训练集的来源，用于创建基于Sloan Digital Sky Survey（SDSS）中具有光谱识别的明智对象的预期数据模型。随后，它标记为异常的那些明智的光度法与该模型不一致的来源。我们报告了36个Bright（$ G_ {AB} $ <19.5）子集的光学和近红外光谱后续观测值的结果，由OCSVM代码标记为“异常”以验证其性能。在观察到的物体中，我们确定了三种主要来源：i）低红移（z〜0.03-0.15）星系，其中包含大量热尘（53％），包括三个狼射线星系； ii）宽线准恒星对象（QSO）（33％），包括低离世的宽吸收线（Lobal）类星体和稀有的QSO，具有强和窄的紫外线铁发射； iii）在其进化的尘埃阶段的银河对象（3％）。由于低信噪或无特征光谱，这些物体中的四个（11％）的性质仍未确定。当前的数据表明，该算法在检测罕见但不一定是最亮的候选物中的对象方面效果很好。它们主要代表原本众所周知的来源的特殊子类型。
We present the results of a programme to search and identify the nature of unusual sources within the All-sky Wide-field Infrared Survey Explorer (WISE) that is based on a machine-learning algorithm for anomaly detection, namely one-class support vector machines (OCSVM). Designed to detect sources deviating from a training set composed of known classes, this algorithm was used to create a model for the expected data based on WISE objects with spectroscopic identifications in the Sloan Digital Sky Survey (SDSS). Subsequently, it marked as anomalous those sources whose WISE photometry was shown to be inconsistent with this model. We report the results from optical and near-infrared spectroscopy follow-up observations of a subset of 36 bright ($g_{AB}$<19.5) objects marked as 'anomalous' by the OCSVM code to verify its performance. Among the observed objects, we identified three main types of sources: i) low redshift (z~0.03-0.15) galaxies containing large amounts of hot dust (53%), including three Wolf-Rayet galaxies; ii) broad-line quasi-stellar objects (QSOs) (33%) including low-ionisation broad absorption line (LoBAL) quasars and a rare QSO with strong and narrow ultraviolet iron emission; iii) Galactic objects in dusty phases of their evolution (3%). The nature of four of these objects (11%) remains undetermined due to low signal-to-noise or featureless spectra. The current data show that the algorithm works well at detecting rare but not necessarily unknown objects among the brightest candidates. They mostly represent peculiar sub-types of otherwise well-known sources.
