arXiv:2606.03574v1 Announce Type: cross 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 hyper
This research leverages recent advancements in few-shot learning and LSTM networks to address a long-standing challenge in astrophysical data analysis, enabled by increasing computational power and AI model sophistication.
Improving the accuracy of pulsar timing predictions can enhance gravitational wave detection capabilities and refine our understanding of fundamental physics, with potential implications for high-precision navigation and timekeeping.
This novel method allows for more accurate analysis of pulsar data with significantly less training data, expanding the utility of limited datasets and potentially accelerating discoveries in astrophysics.
- · Astrophysicists
- · Gravitational wave observatories
- · Machine learning researchers
- · Space exploration programs
- · Traditional statistical methods that require large datasets
More precise pulsar timing residuals become available for analysis.
Enhanced sensitivity for gravitational wave detection using pulsar timing arrays.
Potential for new fundamental physics discoveries or advancements in deep space navigation through ultra-precise timekeeping.
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Read at arXiv cs.LG