AI 中文总结
本文提出基于条件归一化流的AI框架，模拟星系尺度大质量黑洞轨道衰减，成本大幅降低，首次结合宇宙学模拟推断宇宙时间尺度上黑洞双星形成时标，发现恒星棒可改变其形成时间分布。
AI 中文摘要
由星系并合形成的大质量黑洞（MBH）双星可能会在引力波爆发中并合，估算其并合时标和发生率是一个长期存在的天体物理问题，对未来激光干涉空间天线（LISA）等引力波探测器的预测至关重要，但仍具挑战性：在真实星系环境中，MBH轨道衰减过程复杂且随机，恒星棒等非轴对称结构会扰动MBH双星动力学，延缓或加速双星形成，且破坏了仅由动力学摩擦决定旋进时长的假设。要捕捉这种演化过程，所需的模拟在种群尺度上运行成本过高。本文提出一种人工智能框架，利用在大量半解析轨道积分上训练的条件归一化流，模拟旋进MBH的星系尺度轨道衰减。该模型能捕捉次级MBH在包含旋转恒星盘和恒星棒的多成分星系并合遗迹中的轨道演化，覆盖广泛的MBH质量、轨道构型和恒星棒属性范围。训练后的模拟器可复现模拟得到的衰减时间分布，同时将计算成本降低数个数量级。我们首次将该模型应用于宇宙学模拟得出的星系种群，利用有棒和无棒星系的形态信息，推断宇宙时间尺度上MBH双星的形成时标。结果表明，恒星棒可改变MBH双星形成时间的分布；更广泛而言，这证明了基于模拟的替代机器学习模拟器如何能解决一类天体物理问题，这类问题的物理机制对单个系统而言已明确，但在种群尺度上难以处理。
英文摘要
Massive black hole (MBH) pairs, formed in galaxy mergers, may coalesce in a burst of gravitational waves. Estimating the coalescence time-scales and rates is a long-standing astrophysical problem, essential to inform predictions for future gravitational-wave detectors such as the Laser Interferometer Space Antenna, but remains challenging: MBH orbital decay in realistic galactic environments is complex and stochastic, and non-axisymmetric structures such as stellar bars can perturb MBH pair dynamics, delaying or accelerating binary formation and undermining the assumption that dynamical friction alone sets the inspiral duration. Capturing this evolution requires simulations too expensive to run at population scale. Here we present an artificial-intelligence framework that emulates the galactic-scale orbital decay of an inspiralling MBH using conditional normalizing flows trained on a large suite of semi-analytical orbital integrations. Our model captures the evolution of secondary MBHs orbiting within multi-component galactic merger remnants featuring rotating stellar discs and bars, across a broad range of MBH masses, orbital configurations, and bar properties. The trained emulator reproduces the simulations' decay-time distributions while reducing computational cost by orders of magnitude. For the first time, we apply this model to galaxy populations drawn from a cosmological simulation, exploiting morphological information on barred and non-barred galaxies to infer MBH binary formation time-scales across cosmic time. Our results show that stellar bars can alter the distribution of MBH binary formation times. More broadly, this demonstrates how simulation-based, surrogate machine-learning emulators can unlock a class of astrophysical problems where the physics is well understood system-by-system but intractable at scale.
Comments23 pages, submitted to MNRAS
