标题： 使用机器学习在巡天调查成像中进行发现 显示英文标题

标题： Using Machine Learning for Discovery in Synoptic Survey Imaging

摘要： 现代时域巡天持续监测大量天空区域以寻找天文变化。 在这种数据集中，天体发现的复杂性在于真实的瞬变和可变源的检测被由不完美的减法、大气效应和探测器 artifacts 引起的虚假检测大大超过。 在这项工作中，我们提出了一个用于时域成像巡天中可变性发现的机器学习(ML)框架。 我们的ML方法提供了概率陈述，在接近实时的情况下，关于每个新观测到的源成为具有可变亮度的天体物理相关源的程度。 我们提供了每个分析步骤的详细信息，包括训练集和测试集的编译、描述性基于图像和上下文特征的构建，以及特征子集和模型调整参数的优化。 使用来自帕洛玛瞬变工厂的近30,000个对象的验证集，我们展示了在选择的误报率为1%的情况下，对于经过优化的23个特征的ML分类器，漏检率最多为7.7%，这些特征从最初的42个属性库中选出，以避免特征相关性和过拟合。 重要的是，我们表明，我们的分类方法对训练数据中多达10%的错误标记不敏感，这使得在未来调查中更容易编译足够的训练集以获得准确的性能。 如果采用此ML框架，应能最大化未来综合调查的科学收益，并能够对这类实验产生的大量流数据做出快速跟进决策。 显示英文摘要

摘要： Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of real transient and variable sources are highly outnumbered by bogus detections caused by imperfect subtractions, atmospheric effects and detector artefacts. In this work we present a machine learning (ML) framework for discovery of variability in time-domain imaging surveys. Our ML methods provide probabilistic statements, in near real time, about the degree to which each newly observed source is astrophysically relevant source of variable brightness. We provide details about each of the analysis steps involved, including compilation of the training and testing sets, construction of descriptive image-based and contextual features, and optimization of the feature subset and model tuning parameters. Using a validation set of nearly 30,000 objects from the Palomar Transient Factory, we demonstrate a missed detection rate of at most 7.7% at our chosen false-positive rate of 1% for an optimized ML classifier of 23 features, selected to avoid feature correlation and over-fitting from an initial library of 42 attributes. Importantly, we show that our classification methodology is insensitive to mis-labelled training data up to a contamination of nearly 10%, making it easier to compile sufficient training sets for accurate performance in future surveys. This ML framework, if so adopted, should enable the maximization of scientific gain from future synoptic survey and enable fast follow-up decisions on the vast amounts of streaming data produced by such experiments.