AM CVn双星连续谱的新模型及使用归一化流的多信使推断
A new model for the continuum spectra of AM CVn binaries and multi-messenger inference with normalizing flows
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中文总结 AI 辅助
研究针对未来电磁望远镜与引力波探测器联合探测AM CVn双星的需求，提出连接双星参数与多波段观测值的新正向模型，开发用卷积神经网络和归一化流推断参数的框架，为多信使探测及双星天体物理学后续研究奠定基础。
中文摘要 AI 辅助
未来的电磁望远镜，如新雅典娜、CASTOR和类似AXIS的任务，以及像LISA这样的毫赫兹引力波探测器，有望发现银河系中超紧凑双星（UCBs）群体。联合多信使探测将探究诸如AM CVn等进行质量转移的UCBs的形成、演化和可观测性，但理论工具需改进。为此，我们提出了一个新的AM CVn双星连续发射正向模型，将源双星参数与X射线、光学和紫外线可观测值联系起来。该模型假设引力波驱动的质量转移，并对吸积能量学、发射几何、吸收和仪器响应采用基于物理的规定。结合LISA观测和双星群体合成的输出，能够探索AM CVn的多信使特性。我们的模型预测，大约每7000个AM CVn双星中就有一个将允许与LISA、CASTOR和AXIS进行联合多信使探测。我们还开发了一个框架，用于通过卷积神经网络和归一化流从逆模型中推断双星参数。用合成的AM CVn群体测试训练后的流，我们发现推断的吸积体质量、供体质量、轨道周期和距离的平均绝对分数误差分别为0.05 M⊙、0.26 M⊙、0.1 s和0.2 pc，除供体质量外，斯皮尔曼等级显示真实分布和预测分布高度相关。这些努力为后续研究奠定了基础，后续研究将探索详细的双星天体物理学以及未来十年有效多信使科学发现的观测要求。
英文摘要
Future electromagnetic telescopes, such as $\textit{NewAthena}$, $\textit{CASTOR}$, and an $\textit{AXIS}$-like mission, along with milli-Hz gravitational-wave (GW) detectors such as $\textit{LISA}$, are expected to unearth the population of Galactic ultra-compact binaries (UCBs). Joint multi-messenger detections will probe the uncertain formation, evolution, and observables of mass-transferring UCBs such as AM CVns, but theoretical tools need to be advanced to anticipate future data challenges. Motivated by this, we present a new forward model for the continuum emission of AM CVn binaries that connects source binary parameters to X-ray, optical, and ultraviolet observables. The model assumes GW-driven mass transfer with physically motivated prescriptions for accretion energetics, emission geometry, absorption, and instrumental response. Combining this with $\textit{LISA}$ observations and the output of binary population synthesis enables exploration of the multi-messenger properties of AM CVns. Although uncertain, our model predicts that approximately one per $7000$ AM CVn binaries will permit a joint multi-messenger detection with $\textit{LISA}$, $\textit{CASTOR}$, and $\textit{AXIS}$. We also develop a framework for inferring binary parameters from the inverse model with a convolutional neural net and normalizing flows. Testing the trained flow with our synthetic AM CVn population, we find mean absolute fractional error on the inferred accretor mass of $0.05$, donor mass of $0.26$, orbital period of $0.1$, and distance of $0.2$, while Spearman's rank shows strongly correlated true and predicted distributions except for the donor mass. These efforts lay a foundation for follow-up studies that will explore detailed binary astrophysics and observational requirements for effective multi-messenger scientific discovery in the coming decade.
