论文标题
由人工神经网络提供动力的引力波替代模型：波形生成的ANN-SUR
Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
论文作者
论文摘要
推断黑洞和中子星的特性是重力波（GW）天文学的关键科学目标。为了从GW观测值中提取尽可能多的信息，我们必须开发方法来降低贝叶斯推断的成本。在本文中，我们使用人工神经网络（ANN）和图形处理单元（GPU）的并行功率来改善替代建模方法，该方法可以产生现有模型的加速版本。作为我们方法Ann-SUR的第一个应用，我们构建了一个旋转一致的二进制黑洞（BBH）波形模型SEOBNRV4的时间域替代模型。我们达到2E-5的中位数不匹配，不匹配不到2E-3。对于从12 Hz生成的典型BBH波形，总质量为$ 60 m_ \ odot $原始SEOBNRV4型号为1812毫秒。现有的定制代码优化（SEOBNRV4OPT）将其降低到91.6 ms，基于插值的频域代理SEOBNRV4ROM可以在6.9毫秒内生成此波形。当在CPU上运行时，我们的ANN-SUR模型在GPU上运行时需要2.7 ms，仅需0.4 ms。 Ann-SUR还可以同时生成大量波形。我们发现，只需163毫秒即可在GPU上评估高达10^4波形的批次，对应于每次波形为0.016 ms的时间。这种方法是利用GPU的并行能力大大提高贝叶斯参数估计的计算效率的有希望的方法。
Inferring the properties of black holes and neutron stars is a key science goal of gravitational-wave (GW) astronomy. To extract as much information as possible from GW observations we must develop methods to reduce the cost of Bayesian inference. In this paper, we use artificial neural networks (ANNs) and the parallelisation power of graphics processing units (GPUs) to improve the surrogate modelling method, which can produce accelerated versions of existing models. As a first application of our method, ANN-Sur, we build a time-domain surrogate model of the spin-aligned binary black hole (BBH) waveform model SEOBNRv4. We achieve median mismatches of 2e-5 and mismatches no worse than 2e-3. For a typical BBH waveform generated from 12 Hz with a total mass of $60 M_\odot$ the original SEOBNRv4 model takes 1812 ms. Existing bespoke code optimisations (SEOBNRv4opt) reduced this to 91.6 ms and the interpolation based, frequency-domain surrogate SEOBNRv4ROM can generate this waveform in 6.9 ms. Our ANN-Sur model, when run on a CPU takes 2.7 ms and just 0.4 ms when run on a GPU. ANN-Sur can also generate large batches of waveforms simultaneously. We find that batches of up to 10^4 waveforms can be evaluated on a GPU in just 163 ms, corresponding to a time per waveform of 0.016 ms. This method is a promising way to utilise the parallelisation power of GPUs to drastically increase the computational efficiency of Bayesian parameter estimation.
