基于机器学习生成的代理波形的引力波参数估计
Gravitational-wave parameter estimation with machine-learning generated surrogate waveforms
- The University of Tokyo（东京大学）
- Research Center for Early Universe, The University of Tokyo（东京大学早期宇宙研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出两阶段确定性条件自编码器模型生成代理波形，可用于引力波参数估计，虽存在系统偏差但可校正，结合重要性重加权能让低精度代理波形在低信噪比下使用。
AI中文摘要：
截至目前，全球引力波探测器网络已探测到超过350个双致密天体并合事件。未来的第三代探测器（如爱因斯坦望远镜）预计将探测到数量级更多、特征更复杂的信号，包括偏心轨道和高质量比双致密天体的信号。对于这类信号，参数估计的计算成本将极高，而用于似然计算的理论波形预测若能加快生成速度，可显著加速这一过程。为此，近期已提出多种机器学习技术。本研究提出一种两阶段确定性条件自编码器（conditional-autoencoder）模型，用于生成四参数SEOBNRv4波形。模型第一阶段生成波形的振幅和相位序列，第二阶段校准预测中的残余误差。该模型与目标偏振波形的失配度中位数约为10⁻²，经校准的振幅/相位序列达到10⁻⁶量级的余弦距离误差。我们进一步提出波形条件化步骤，使这些代理波形可用于下游参数估计任务。最后，我们开展大量参数估计测试，注入机器学习（ML）波形和EOB波形，尝试恢复源参数的后验估计。研究发现，当用ML波形恢复EOB目标参数估计时，推断的后验存在一定系统偏差；这种固有偏差可被估计并校正，而后验样本的重要性重加权可使低精度代理波形在低信噪比（SNR）下得以应用。
英文摘要：
The worldwide network of gravitational-wave detectors have detected more than 350 binary coalescence events till date. Future third-generation detectors, like Einstein telescope, are expected to detect orders-of-magnitude more signals from sources with more complicated characteristics, including eccentric orbits and high-mass ratio binaries. It is well-established that the computational cost of parameter estimation for signals from these kinds of sources will be extremely high. In particular, the process could be sped-up if generating theoretical waveform predictions, used for likelihood calculation becomes faster. Recently, various machine-learning techniques has been proposed to this end. In this work, we propose a two-stage deterministic conditional-autoencoder model for generating four-parameter SEOBNRv4 waveforms. The first-stage of the model generates amplitude and phase series of the waveform, while the second-stage calibrates the residual error in the predictions. Our model achieves a median mismatch of around $10^{-2}$ with the target polarization waveforms, while the calibrated amplitude/phase series achieve $10^{-6}$ level cosine distance error. We then propose a waveform conditioning step to enable use of these surrogate waveforms for downstream parameter estimation tasks. Finally, we perform extensive parameter estimation tests, with ML and EOB waveform injections and try to recover posterior estimates for the source parameters. We find that when ML waveforms are used to recover EOB target parameter estimates, the inferred posterior have some systematic bias. This inherent bias can be estimated and corrected for, and then importance reweighting of posterior samples can enable use of low-accuracy surrogate waveforms at low SNRs.
