论文标题
行星碰撞的现实结果II：将机器学习带到N体模拟
Realistic On-the-fly Outcomes of Planetary Collisions II: Bringing Machine Learning to N-body Simulations
论文作者
论文摘要
陆地行星形成理论正处于瓶颈，越来越多地认识到成对碰撞的治疗太简单了。在这里，在我们的同伴论文（Cambioni等人，2019年）中引入了培训方法，我们证明了机器学习的第一个应用，以更真实地对巨型影响进行巨大影响。我们提出了替代模型，这些模型为巨大冲击的两个最大残余物的质量和速度提供了快速，可靠的答案，这是碰撞质量及其冲击速度和角度的函数，我们的训练数据尚未包括前型旋转或可变的热条件。我们将陆地行星形成的规范N体情况与假设完美合并（Chambers 2001）与我们更现实的治疗方法进行了比较，该疗法包括效率低下的积聚和撞击碰撞。结果是持续约200 Myr的最终事件的旷日持久的尾巴，以及大约一半的初始种群向碎片的转化。我们获得了完全不同的太阳系体系结构，具有更广泛的陆地行星质量和增强的构图多样性。
Terrestrial planet formation theory is at a bottleneck, with the growing realization that pairwise collisions are treated far too simply. Here, and in our companion paper (Cambioni et al. 2019) that introduces the training methodology, we demonstrate the first application of machine learning to more realistically model the late stage of planet formation by giant impacts. We present surrogate models that give fast, reliable answers for the masses and velocities of the two largest remnants of a giant impact, as a function of the colliding masses and their impact velocity and angle, with the caveat that our training data do not yet include pre-impact rotation or variable thermal conditions. We compare canonical N-body scenarios of terrestrial planet formation assuming perfect merger (Chambers 2001) with our more realistic treatment that includes inefficient accretions and hit-and-run collisions. The result is a protracted tail of final events lasting ~200 Myr, and the conversion of about half the mass of the initial population to debris. We obtain profoundly different solar system architectures, featuring a much wider range of terrestrial planet masses and enhanced compositional diversity.
