arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.14847astro-ph.GA
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星系合并中形成的条及其深度学习分类

Bars formed in galaxy merging and their classification with deep learning

Mitchell Cavanagh, Kenji Bekki

摘要:

星条是旋涡星系的一个常见形态特征。虽然已知它们可以孤立地形成,也可以潮汐诱导,但很少有研究探索星系合并中恒星棒的产生。我们希望使用深度学习的方法来分析我们的N体模拟来研究星系合并中的酒吧形成。主要目的是确定最有利于棒形成的合并星系的质量比和方向的约束。我们进一步的目标是探索是否有可能根据它们的形成机制对模拟的棒旋星系进行分类。我们测试了这种新的分类方案与模拟星系的可行性。使用从我们的模拟中获得的一组29,400张图像,我们首先训练了一个卷积神经网络来区分棒星系和非棒星系。然后,我们在不同质量比和自旋角的模拟上测试了网络。我们调整了核心神经网络架构,以用于我们的其他目标。我们发现,一个强烈的质量比和生产的酒吧的数量之间的反比关系。我们还确定了两个不同的阶段,在酒吧的形成过程中;(1)最初的,潮汐诱导形成合并前,(2)的破坏和/或再生的合并过程中和之后。与等质量合并相比,具有低质量比和紧密对齐取向的合并更有利于酒吧的形成。我们通过展示基于其形成机制对酒吧进行分类是可行的,展示了我们的深度学习方法的灵活性。

英文摘要:

Stellar bars are a common morphological feature of spiral galaxies. While it is known that they can form in isolation, or be induced tidally, few studies have explored the production of stellar bars in galaxy merging. We look to investigate bar formation in galaxy merging using methods from deep learning to analyse our N-body simulations. The primary aim is to determine the constraints on the mass ratio and orientations of merging galaxies that are most conducive to bar formation. We further aim to explore whether it is possible to classify simulated barred spiral galaxies based on the mechanism of their formation. We test the feasibility of this new classification schema with simulated galaxies. Using a set of 29,400 images obtained from our simulations, we first trained a convolutional neural network to distinguish between barred and non-barred galaxies. We then tested the network on simulations with different mass ratios and spin angles. We adapted the core neural network architecture for use with our additional aims. We find that a strong inverse relationship between mass ratio and the number of bars produced. We also identify two distinct phases in the bar formation process; (1) the initial, tidally induced formation pre-merger, and (2) the destruction and/or regeneration of the during and after the merger. Mergers with low mass ratios and closely-aligned orientations are considerably more conducive to bar formation compared to equal-mass mergers. We demonstrate the flexibility of our deep learning approach by showing it is feasible to classify bars based on their formation mechanism.

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。

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