Posterior inference from pulsar observations in the form of light curves is commonly performed using Markov chain Monte Carlo methods, which are accurate but computationally expensive. We introduce a framework that accelerates posterior inference while maintaining accuracy by combining learned latent representations with local simulator-guided optimization. A masked U-Net is first pretrained to reconstruct complete light curves from partial observations and to produce informative latent embeddings. Given a query light curve, we identify similar simulated light curves from the simulation bank by measuring similarity in the learned embedding space produced by pretrained U-Net encoder, yielding an initial empirical approximation to the posterior over parameters. This initialization is then refined using a local optimization procedure using hill-climbing updates, guided by a forward simulator, progressively shifting the empirical posterior toward higher-likelihood parameter regions. Experiments on the observed light curve of PSR J0030+0451, captured by NASA's Neutron Star Interior Composition Explorer (NICER), show that our method closely matches posterior estimates obtained using traditional MCMC methods while achieving 120 times reduction in inference time (from 24 hours to 12 minutes), demonstrating the effectiveness of learned representations and simulator-guided optimization for accelerated posterior inference.

翻译：从脉冲星观测数据（以光变曲线形式）进行后验推断通常采用马尔可夫链蒙特卡洛方法，该方法精度高但计算成本昂贵。本文提出一种结合学习潜在表征与局部仿真器引导优化的框架，在保持精度的同时加速后验推断。首先预训练一个掩码U-Net，用于从部分观测数据重建完整光变曲线，并生成信息丰富的潜在嵌入。给定查询光变曲线，我们通过计算预训练U-Net编码器产生的学习嵌入空间中的相似度，从仿真数据库中识别相似的模拟光变曲线，从而获得参数后验的初始经验近似。随后，在前向仿真器的引导下，采用基于爬山更新的局部优化程序对该初始化进行细化，逐步将经验后验向更高似然度的参数区域移动。在NASA中子星内部组成探测器（NICER）观测的PSR J0030+0451光变曲线上的实验表明，本方法与传统MCMC方法获得的后验估计高度吻合，同时将推断时间缩短120倍（从24小时降至12分钟），验证了学习表征与仿真器引导优化在加速后验推断中的有效性。