用卷积神经网络确定相互作用星系对的相对倾角和viewing angle
Determination of the relative inclination and the viewing angle of an interacting pair of galaxies using convolutional neural networks
摘要：
建立相互作用星系对的动力学模型，必须考虑到星系对的观测结构和运动学特征，关键在于正确选择星系面之间的相对倾角（$i$）和观测角（$θ$），即视线与轨道运动面法线之间的夹角。我们构建深度卷积神经网络（DCNN）模型来确定相互作用星系对的相对倾角（$i$）和视角（$θ$），使用GALMER数据库中的N体$+$平滑粒子流体动力学（SPH）模拟数据进行训练。为了仅根据它们的$i$值对星系对进行分类，我们首先构建了（a）2类（$i$ = 0 $^{\circ}$，45 $^{\circ}$）和（b）3类（$i = 0^{\circ}, 45^{\circ} \text{ and } 90^{\circ}$）分类的DCNN模型，分别获得了99%和98%的$F_1$分数。此外，对于基于$i$和$θ$值的分类，我们开发了用于9类分类（$(i,θ) \sim (0^{\circ},15^{\circ}) ,(0^{\circ},45^{\circ}), (0^{\circ},90^{\circ}), (45^{\circ},15^{\circ}), (45^{\circ}, 45^{\circ}), (45^{\circ}, 90^{\circ}), (90^{\circ}, 15^{\circ}), (90^{\circ}, 45^{\circ}), (90^{\circ},90^{\circ})$）的DCNN模型，并且$F_1$得分为97 $\%$。最后，我们测试了我们的2类模型的实际数据的相互作用星系对从斯隆数字巡天（SDSS）DR 15，并实现了$F_1$得分为78%。我们的DCNN模型可以进一步扩展，以确定为相互作用的星系对的动力学建模所需的额外参数，这是目前通过试错法完成的。
英文摘要：
Constructing dynamical models for interacting pair of galaxies as constrained by their observed structure and kinematics crucially depends on the correct choice of the values of the relative inclination ($i$) between their galactic planes as well as the viewing angle ($θ$), the angle between the line of sight and the normal to the plane of their orbital motion. We construct Deep Convolutional Neural Network (DCNN) models to determine the relative inclination ($i$) and the viewing angle ($θ$) of interacting galaxy pairs, using N-body $+$ Smoothed Particle Hydrodynamics (SPH) simulation data from the GALMER database for training the same. In order to classify galaxy pairs based on their $i$ values only, we first construct DCNN models for a (a) 2-class ( $i$ = 0 $^{\circ}$, 45$^{\circ}$ ) and (b) 3-class ($i = 0^{\circ}, 45^{\circ} \text{ and } 90^{\circ}$) classification, obtaining $F_1$ scores of 99% and 98% respectively. Further, for a classification based on both $i$ and $θ$ values, we develop a DCNN model for a 9-class classification ($(i,θ) \sim (0^{\circ},15^{\circ}) ,(0^{\circ},45^{\circ}), (0^{\circ},90^{\circ}), (45^{\circ},15^{\circ}), (45^{\circ}, 45^{\circ}), (45^{\circ}, 90^{\circ}), (90^{\circ}, 15^{\circ}), (90^{\circ}, 45^{\circ}), (90^{\circ},90^{\circ})$), and the $F_1$ score was 97$\%$. Finally, we tested our 2-class model on real data of interacting galaxy pairs from the Sloan Digital Sky Survey (SDSS) DR15, and achieve an $F_1$ score of 78%. Our DCNN models could be further extended to determine additional parameters needed to model dynamics of interacting galaxy pairs, which is currently accomplished by trial and error method.
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