VAMANA：二元黑洞布居的最小假设模型
VAMANA: Modeling Binary Black Hole Population with Minimal Assumptions
摘要：
在对致密双星进行星族分析时，需要对观测数据中的一些引力波信号参数，如质量和自旋分布等进行重建。本文介绍VAMANA，它使用混合模型重建二元黑洞种群，并根据数据进行出色的密度测量。VAMANA使用加权高斯的混合来重建啁啾质量分布。我们期望高斯混合物提供灵活性，在模拟复杂的分布，使我们能够捕捉细节的天体物理啁啾质量分布。混合物中的每一个高斯与另一个高斯和幂律相结合，以同时模拟与轨道角动量和质量比分布对齐的自旋分量，从而也使我们能够捕获它们随啁啾质量的变化。此外，我们还可以通过将高斯混合限制在预定义的参考啁啾质量分布的阈值距离内来引入宽带平滑。使用模拟数据，我们展示了我们的方法在重建复杂的人口大量的观察的鲁棒性。我们还将我们的方法应用于LIGO和Virgo的第一次和第二次观测运行期间所做的GW观测的公开目录，并提出了重建的质量，自旋分布和估计的合并率的二进制黑洞。
英文摘要：
The population analysis of compact binaries involves the reconstruction of some of the gravitational wave (GW) signal parameters, such as, the mass and the spin distribution, that gave rise to the observed data. This article introduces VAMANA, which reconstructs the binary black hole population using a mixture model and facilitates excellent density measurement as informed by the data. VAMANA uses a mixture of weighted Gaussians to reconstruct the chirp mass distribution. We expect Gaussian mixtures to provide flexibility in modeling complex distributions and enable us in capturing details in the astrophysical chirp mass distribution. Each of the Gaussian in the mixture is combined with another Gaussian and a power-law to simultaneously model the spin component aligned with the orbital angular momentum and the mass ratio distribution, thus also allowing us to capture their variation with the chirp mass. Additionally, we can also introduce broadband smoothing by restricting the Gaussian mixture to lie within a threshold distance of a predefined reference chirp mass distribution. Using simulated data we show the robustness of our method in reconstructing complex populations for a large number of observations. We also apply our method to the publicly available catalog of GW observations made during LIGO's and Virgo's first and second observation runs and present the reconstructed mass, spin distribution, and the estimated merger rate of binary black holes.
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