This work proposes a novel solution to predict pulsar timing residuals with limited data, addressing the critical challenge of data scarcity across spin-frequency subgroups of millisecond pulsars in PTA datasets. The proposed solution applies a Long Short-Term Memory (LSTM) network optimized using the model-agnostic meta-learning algorithm, enabling rapid adaptation to new frequency domain by fine-tuning the LSTM network with only a few-shot of ground truth timing residuals. Particle swarm optimization algorithm is also used for automatic hyperparameter optimization, leading to improved prediction accuracy. Our solution, evaluated on the second data release of the International Pulsar Timing Array (IPTA), demonstrates robust generalization with accurate predictions in three metrics across high-frequency test frequency domains, while requiring only 10% of the timing residuals from these domains for model fine-tuning. Furthermore, our lightweight structure only costs 16.86 MB CPU memory and 18 milliseconds for single-step residual prediction. All these characteristics make our solution highly suitable for real-world applications, where effective and real-time predictions of pulsar timing residuals are essential-particularly in resource-constrained environments with limited computational power, memory, or energy availability.

翻译：本文提出了一种新颖的解决方案，用于在数据有限的情况下预测脉冲星计时残差，解决了PTA数据集中毫秒脉冲星自旋频率子群数据稀缺的关键挑战。该方案采用基于模型无关元学习算法优化的长短期记忆网络，通过仅利用少量真实计时残差对LSTM网络进行微调，实现了对新型频率域的快速自适应。同时采用粒子群优化算法进行超参数自动优化，从而提升预测精度。该方案在国际脉冲星计时阵列第二次数据发布中进行了评估，在高频测试频域的三个指标上展现出稳健的泛化能力与精确预测性能，且仅需各频域10%的计时残差即可完成模型微调。此外，其轻量化结构在单步残差预测中仅需16.86 MB CPU内存和18毫秒计算时间。这些特性使该方案特别适用于对脉冲星计时残差进行高效实时预测的实际应用场景，尤其在计算能力、内存或能源有限的资源受限环境中。