一种针对MWISP项目分子云团块的3D CNN证认方法

分子云团块是恒星的诞生地. 分子团块的普查和其性质的全面研究将有助于了解恒星的形成乃至星系和宇宙的演化过程. 随着银河画卷计划(MWISP)项目的深入进行, 这类研究方案变得切实可行. 但是项目产生的分子云观测数据是海量的, 因此迫切需要一种能够自动识别和证认分子团块的方法. 目前应用广泛的3维分子云数据处理方法有很多, 典型的包括GaussClumps、ClumpFind、FellWalker、Reinhold等, 但都需要输入多个参数来控制它们的性能, 并且进行反复的参数优化和目测才能得到比较满意的结果. 对于大规模的观测数据, 利用现有方法进行分子团块的证认将是一项耗时耗力的任务. 为了克服传统分子云团块检测算法的局限性, 人工智能(AI)的方法将提供一个很好的解决方案. 提出了一种3D CNN (Convolutional Neural Network)方法, 它可以自动处理3D分子谱线数据, 整个过程分为检出和验证两个步骤. 首先, 通过设置较低阈值使用ClumpFind以检出候选对象, 然后通过训练好的3D CNN模型进行验证. 利用仿真数据所做的一系列的实验结果表明, 该方法的综合表现优于4种传统方法. 将该方法应用于实际的MWISP数据表明, 3D CNN方法的性能也令人满意.

A 3D CNN Molecular Clump Verification Method for MWISP Project

Molecular clumps are the birth place of stars. A census of molecular clumps and comprehensive studying of their properties will help us to understand the star formation process and the evolution of the Galaxy and the Universe. As the MWISP (Milky Way Imaging Scroll Painting) project going to be completed, such a kind of studies become to be practicably feasible. With the large amount of data at hand, an algorithm that automatically identifies and verifies molecular clumps is urgently in need. For the widely used methods for the three-dimensional molecular line data, including GaussClumps, ClumpFind, FellWalker, and Reinhold, one has to input a number of parameters to control their performances, which need to be repeatedly optimized and visually inspected, and then to obtain a satisfactory result. Therefore it is a human-power and time consuming task to identify and verify clumps for large-scale survey data. To overcome the limitations of the traditional clump detection algorithms, artificial intelligence (AI) would be a good solution. Here we propose a 3D CNN (Convolutional Neural Network) method, which can perform the task automatically. The whole process is divided into two steps, i.e., identification and verification. First we use traditional method (ClumpFind) with low threshold to identify candidates. The verification is done by trained 3D CNN models. We have done a series of experiments using artificial data. The results suggest that our method is advantageous over the four traditional methods. Application of the method to the real MWISP data demonstrates that the performance of the 3D CNN method is also satisfactory.