论文标题
使用神经网络在4FGL的非相关来源之间区分新发现的BL LAC和FSRQ，该来源采用了伽马射线，X射线，UV/uv/optical和IR数据
Using Neural Networks to Differentiate Newly Discovered BL Lacs and FSRQs among the 4FGL Unassociated Sources Employing Gamma-ray, X-ray, UV/Optical and IR Data
论文作者
论文摘要
在第四个费米目录的第三个数据释放（DR3）中的〜2157个非相关源中，使用了尼尔·盖尔斯（Neil Gehrels）迅速观测器尖头仪器观察到〜1200。这些观察结果在95％的费米不确定性区域内产生了238个高S/N X射线源。最近，Kerby等。使用神经网络，可以在与4FGL无关的238个X射线对应物中找到Blazar候选者，并发现112个可能的Blazar可能对应物来源。 Blazars的完整样本及其亚分类是有助于了解Blazar序列的难题以及Fermi目录中Gamma-ray发射Blazar类的整体完整性的必要步骤。我们使用伽马射线，X射线，UV/uv/optical和ir属性，在这112个Blazar候选者中使用了多个PERCEPTRON神经网络分类器来识别这112个Blazar候选者之间的FSRQ和BL lac。该分类器为每个源提供了与一个或另一个类别关联的概率估计，因此P_FSRQ表示与FSRQ子类关联的源的概率。使用这种方法，以> 99％的置信度将4个FSRQ和50个BL LAC分类为归类，而其余的58个Blazar则不能明确将其归类为BL LAC或FSRQ。
Among the ~2157 unassociated sources in the third data release (DR3) of the fourth Fermi catalog, ~1200 were observed with the Neil Gehrels Swift Observatory pointed instruments. These observations yielded 238 high S/N X-ray sources within the 95% Fermi uncertainty regions. Recently, Kerby et al. employed neural networks to find blazar candidates among these 238 X-ray counterparts to the 4FGL unassociated sources and found 112 likely blazar counterpart sources. A complete sample of blazars, along with their sub-classification, is a necessary step to help understand the puzzle of the blazar sequence and for the overall completeness of the gamma-ray emitting blazar class in the Fermi catalog. We employed a multi-perceptron neural network classifier to identify FSRQs and BL Lacs among these 112 blazar candidates using the gamma-ray, X-ray, UV/optical, and IR properties. This classifier provided probability estimates for each source to be associated with one or the other category, such that P_fsrq represents the probability for a source to be associated with the FSRQ subclass. Using this approach, 4 FSRQs and 50 BL Lacs are classified as such with >99% confidence, while the remaining 58 blazars could not be unambiguously classified as either BL Lac or FSRQ.
