Integrating human and machine intelligence in galaxy morphology classification tasks
Integrating human and machine intelligence in galaxy morphology classification tasks
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DOI：
10.1093/mnras/sty503
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发表时间：
2018-06-01
影响因子：
4.8
通讯作者：
Wright, Darryl
中科院分区：
文献类型：
--
作者：
Beck, Melanie R.;Scarlata, Claudia;Wright, Darryl
量化星系形态是一项具有挑战性但具有科学回报的任务。随着即将到来的调查数据规模不断增加，传统的分类方法将难以处理负载。我们通过集成视觉和自动分类提出了一种解决方案，保留了人类和机器的最佳特征。我们通过重新分析 Galaxy Zoo 2 (GZ2) 项目期间收集的视觉星系形态分类来证明该系统的有效性。我们使用名为 SWAP 的贝叶斯分类聚合算法重新处理 GZ2 决策树的顶层问题，该算法最初是为 Space Warps 引力透镜项目开发的。通过简单的二元分类方案，我们将分类率提高了近 5 倍，在 92 天的 GZ2 项目时间内对 226 124 个星系进行了分类，同时以 95.7% 的准确度再现了从 GZ2 分类数据得出的标签。接下来，我们将其与随机森林机器学习算法相结合，该算法学习一系列广泛用于自动化形态学的非参数形态指标。我们开发了一个决策引擎，在人类和机器之间委派任务，并证明组合系统的分类率至少提高了 8 倍，在 GZ2 项目的短短 32 天内对 210 803 个星系进行了分类，准确率高达 93.1%。由于随机森林算法需要最少的计算成本，这一结果对于欧几里得时代和其他大规模巡天的星系形态识别任务具有重要意义。
Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.