论文标题
在银河合并中形成的酒吧及其与深度学习的分类
Bars formed in galaxy merging and their classification with deep learning
论文作者
论文摘要
恒星条是螺旋星系的常见形态特征。虽然众所周知，它们可以孤立地形成或潮汐引起，但很少有研究探讨了星系合并中恒星条的产生。我们希望使用深度学习的方法研究银河系合并中的条形形成，以分析我们的N体模拟。主要目的是确定最有利于棒形成的星系的质量比和方向的约束。我们进一步旨在探索是否可以根据其形成机制对模拟的固定螺旋星系进行分类。我们使用模拟星系测试了该新分类模式的可行性。使用从我们的模拟获得的一组29,400张图像，我们首先训练了一个卷积神经网络，以区分被禁止的星系和非键的星系。然后，我们在具有不同质量比和旋转角度的模拟上测试了网络。我们对核心神经网络体系结构进行了调整，以与我们的其他目标一起使用。我们发现，质量比与产生的棒数之间存在牢固的反相关关系。我们还确定了条形成过程中的两个不同阶段。 （1）最初的潮汐诱导的形成前合并和（2）合并期间和之后的破坏和/或再生。与等量合并相比，质量比和紧密平整方向的合并更为有利于棒形成。我们通过证明棒可以根据其形成机制对其进行分类是可行的，从而证明了我们深度学习方法的灵活性。
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.
