机器学习能否提升引力波背景的可探测性与解纠缠能力？
Can machine learning improve the detectability and disentanglement of the gravitational-wave background?
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中文总结 AI 辅助
本研究提出定制深度学习多尺度多头自编码器结合MCMC推断的方法，可提升引力波背景的可探测性与解纠缠能力，在模拟数据集上表现优于标准pygwb管道。
中文摘要 AI 辅助
致密并合双星与早期宇宙过程产生的引力波预计会形成引力波背景。我们采用定制的深度学习多尺度多头自编码器架构来从探测器噪声中分离引力波背景，随后通过马尔可夫链蒙特卡洛（MCMC）推断阶段区分天体物理与宇宙学分量。通过分析代表LIGO-Virgo-KAGRA第四轮观测运行初期的108天模拟数据集，我们表明：在参考频率f_ref=25Hz处，振幅为4.3⁺⁰·⁵₋₀·₄×10⁻⁹的致密并合双星引力波背景可被高置信度探测，其对数噪声贝叶斯因子大于3，该振幅约为致密源预期振幅的5倍；我们还表明，在模拟的、模拟LIGO第四轮观测运行灵敏度的高斯噪声中，可从预期的致密并合双星引力波背景中分离出弱至9.7⁺²·⁵₋₂·₄×10⁻¹⁰的宇宙学引力波背景（假设为平坦谱）。在与标准pygwb管道的盲测对比中，我们的方法实现了更准确的振幅与谱指数恢复，且能分离天体物理与宇宙学背景分量。
英文摘要
Gravitational waves from compact binary coalescences and from early Universe processes are expected to form a gravitational-wave background. We employ a custom deep learning multi-scale multi-headed autoencoder architecture to isolate gravitational-wave background from detector noise, followed by a Markov chain Monte Carlo inference stage to separate the astrophysical and cosmological components. Analyzing $108$-day mock datasets representative of the first period of the fourth LIGO-Virgo-KAGRA observing run, we show that we can detect with high confidence --- $\log_{10}$ noise Bayes factor larger than 3 --- a compact binary coalescence gravitational-wave background with an amplitude of $4.3^{+0.5}_{-0.4}\times10^{-9}$ at $f_{\rm ref}=25\,\mathrm{Hz}$, which is a factor $\sim5$ higher than the amplitude expected from compact binary sources. We also show that we can isolate a cosmological -- assumed flat spectrum -- gravitational-wave background as weak as $ 9.7^{+2.5}_{-2.4} \times 10^{-10}$ from the expected compact binary coalescence gravitational-wave background within simulated Gaussian noise mimicking the LIGO detectors sensitivity achieved in the fourth observing run. In blind-test comparisons with the standard \texttt{pygwb} pipeline, we show that our method achieves more accurate amplitude and spectral-index recovery and enables the separation of astrophysical and cosmological background components.
