论文标题
使用卷积神经网络确定相互作用的星系的相对倾向和视角
Determination of the relative inclination and the viewing angle of an interacting pair of galaxies using convolutional neural networks
论文作者
论文摘要
构建动态模型，用于相互作用的星系对，受其观察到的结构和运动学的约束，至关重要地取决于正确选择相对倾斜的值（$ i $）之间的银河平面以及视角（$θ$），即视线和视线之间的角度和轨道运动平面之间的角度。我们使用N-Body $+$平滑的粒子流体动力学（SPH）仿真数据来确定相互作用的星系对的相对倾向（$ i $）和视角（$θ$）的相对倾向（$ i $）和观看角度（$θ$），从Galmer数据库进行训练。为了仅根据其$ i $值对星系对进行分类，我们首先构建了（a）2级（$ i $ = 0 $ = 0 $ = 0 $^{\ circ} $，45 $^{\ circ} $）和（b）3级（$ i = 0^{\ circ}，45^{\ cirp} $ cirp} $ cool {分类，分别获得99％和98％的$ F_1 $分数。此外，对于基于$ i $和$θ$值的分类，我们为9级分类（$（i，θ）\ sim（0^{\ circ}，15^{\ circ}））开发DCNN模型（45^{\ circ}，15^{\ circ}），（45^{\ circ}，45^{\ circ}），（45^{\ circ}，90^{\ circ}），（90^{\ circ}，15^{\ circ}，circ} {\ circ} {\ 90}} （90^{\ circ}，90^{\ circ}）$），$ f_1 $得分为97 $ \％$。最后，我们在斯隆数字天空调查（SDSS）DR15的相互作用星系对的真实数据上测试了我们的2级模型，并获得了78％的$ F_1 $得分。我们的DCNN模型可以进一步扩展，以确定建模相互作用星系对的动态所需的其他参数，这是当前通过反复试验完成的。
Constructing dynamical models for interacting pair of galaxies as constrained by their observed structure and kinematics crucially depends on the correct choice of the values of the relative inclination ($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.
