AI RESEARCH
Few-Shot Prediction for Pulsar Noise with Long Short-Term Memory Network
arXiv CS.LG
•
ArXi:2606.03574v1 Announce Type: cross 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.