arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1711.03480astro-ph.GA
收藏

机器学习算法在粉尘排放气泡识别中的性能评估

Assessing the Performance of a Machine Learning Algorithm in Identifying Bubbles in Dust Emission

Duo Xu, Stella S. R. Offner

备注：
ApJ accepted
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

由大质量恒星的辐射和风产生的恒星反馈在分子云的物理和化学演化中起着重要作用。这种能量和动量会留下一个可识别的信号（“气泡”），影响云的动力学和结构。大多数气泡搜索都是“通过眼睛”进行的，这通常是耗时的，主观的，难以校准。基于机器学习的自动分类使得对气泡进行系统的、可量化的和可重复的搜索成为可能。我们采用了以前开发的机器学习算法，Brut，并定量评估其性能，在识别气泡使用合成粉尘观测。我们采用磁流体动力学模拟，它模拟恒星风在湍流分子云内发射，作为生成合成图像的输入。我们使用一个公开可用的三维尘埃连续蒙特卡罗辐射转移代码，hypap，生成合成图像的气泡在三个斯皮策波段（4.5微米，8微米和24微米）。我们将一半的合成气泡指定为训练集，我们用它来训练布鲁特，以及来自银河系项目的公民科学数据。然后，我们评估布鲁特的准确性使用剩余的合成观测。我们发现，重新训练后，布鲁特的性能显着增加，它能够识别黄色的气泡，这可能与B型恒星。Brut继续在先前识别的高分气泡上表现良好，超过10%的银河系项目气泡被重新分类为高置信度气泡，这些气泡在银河系项目数据中以前是边缘或模糊的检测。我们还研究了训练集的大小、尘埃模型、演化阶段和背景噪声对气泡识别的影响。

英文摘要：

Stellar feedback created by radiation and winds from massive stars plays a significant role in both physical and chemical evolution of molecular clouds. This energy and momentum leaves an identifiable signature ("bubbles") that affect the dynamics and structure of the cloud. Most bubble searches are performed "by-eye", which are usually time-consuming, subjective and difficult to calibrate. Automatic classifications based on machine learning make it possible to perform systematic, quantifiable and repeatable searches for bubbles. We employ a previously developed machine learning algorithm, Brut, and quantitatively evaluate its performance in identifying bubbles using synthetic dust observations. We adopt magneto-hydrodynamics simulations, which model stellar winds launching within turbulent molecular clouds, as an input to generate synthetic images. We use a publicly available three-dimensional dust continuum Monte-Carlo radiative transfer code, hyperion, to generate synthetic images of bubbles in three Spitzer bands (4.5 um, 8 um and 24 um). We designate half of our synthetic bubbles as a training set, which we use to train Brut along with citizen-science data from the Milky Way Project. We then assess Brut's accuracy using the remaining synthetic observations. We find that after retraining Brut's performance increases significantly, and it is able to identify yellow bubbles, which are likely associated with B-type stars. Brut continues to perform well on previously identified high-score bubbles, and over 10% of the Milky Way Project bubbles are reclassified as high-confidence bubbles, which were previously marginal or ambiguous detections in the Milky Way Project data. We also investigate the size of the training set, dust model, evolution stage and background noise on bubble identification.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑