发表机构
Department of Physics, National University of Singapore; Department of Mathematics, National University of Singapore（新加坡国立大学物理系; 新加坡国立大学数学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用含物理信息编码框架的Transformer从脉冲星计时阵列数据高效推断相对论轨道上偏心双黑洞，用生成模型在模拟推理框架中推断后验分布，相比无物理基线有改进。
AI 中文摘要
脉冲星计时阵列（PTA）为纳赫兹引力波（GWs）提供独特窗口，但用传统贝叶斯技术从噪声长基线计时残差中提取天体物理参数具有计算挑战性。我们引入具有物理信息位置编码框架的Transformer，通过结构化位置编码将GW相位演化嵌入模型，用生成模型在模拟推理框架中推断后验分布，该方法有改进。
英文摘要
Pulsar timing arrays (PTAs) provide a unique window into nanohertz gravitational waves (GWs), but extracting astrophysical parameters from noisy, long-baseline timing residuals remains computationally challenging with traditional Bayesian techniques due to the high dimensionality of the parameter space, complex and correlated noise models, and the cost of repeated likelihood evaluations. We introduce a Transformer with a physics-informed positional-encoding framework for the efficient inference of eccentric binary black holes in relativistic orbits from PTA data. Our approach embeds analytical GW phase evolution directly into the model through structured positional encodings, enabling the network to learn physically meaningful representations from raw PTA timing residuals. We then use generative models, including discrete and continuous conditional normalizing flows, to infer posterior distributions within a simulation-based inference framework. Across a range of signal-to-noise ratios, the proposed method achieves improved accuracy, sharper posteriors, and faster inference compared to physics-agnostic baselines. While presented for deterministic white-noise signals, the modular framework readily generalizes to realistic PTA analyses incorporating red noise and additional components. This work highlights the potential of physics-aware deep learning models as scalable alternatives to conventional inference pipelines for next-generation PTA datasets.
Comments24 pages, 7 figures, 4 tables
