Few-Shot Prediction for Pulsar Noise with Long Short-Term Memory Network

2026-06-02

AI Summary

This study addresses the few-shot learning challenge in millisecond pulsar timing residual prediction, which arises from data scarcity within spin frequency subgroups. To overcome this limitation, the authors propose a novel approach that integrates Long Short-Term Memory (LSTM) networks, Model-Agnostic Meta-Learning (MAML), and Particle Swarm Optimization (PSO). This work is the first to apply meta-learning to pulsar noise modeling, enabling rapid cross-frequency-domain generalization with only 10% of the target-domain timing residuals required for high-accuracy prediction. Experiments on the second International Pulsar Timing Array (IPTA) dataset demonstrate superior performance across three evaluation metrics. The model achieves single-step inference with just 16.86 MB memory usage and 18 ms latency, exhibiting both lightweight architecture and low computational overhead, making it well-suited for resource-constrained applications.

Abstract

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.

Problem

• Few-Shot Prediction
• Pulsar Timing Residuals
• Data Scarcity
• Millisecond Pulsars
• Frequency Subgroups

Innovation

• Few-shot learning
• Long Short-Term Memory (LSTM)
• Model-agnostic meta-learning (MAML)
• Pulsar timing residuals
• Particle swarm optimization