Few-Shot Prediction for Pulsar Noise with Long Short-Term Memory Network
2026-06-02 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new method to predict small changes in the timing of millisecond pulsars, even when there is very little data available. They use a special type of neural network called LSTM combined with a learning technique that helps the model quickly adapt to new data with only a few examples. Their approach also includes an automatic way to find the best settings for the model, making predictions accurate and efficient. Tests on real pulsar datasets show the method works well and uses very little computing power, making it practical for real-time use in limited-resource settings.
pulsar timing residualsmillisecond pulsarsLong Short-Term Memory (LSTM)model-agnostic meta-learning (MAML)particle swarm optimizationInternational Pulsar Timing Array (IPTA)few-shot learninghyperparameter optimizationtime series predictionresource-constrained computing
Authors
Qingye Tang, Dechao An, Haoran Peng, Yuqi Ouyang
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.