Pulsar Detection with Deep Learning

• URL: http://arxiv.org/abs/2510.25774v1
• Date: Sun, 26 Oct 2025 05:12:45 GMT
• Title: Pulsar Detection with Deep Learning
• Abstract summary: This thesis builds a deep learning pipeline for radio pulsar candidate selection that fuses array-derived features with image diagnostics.<n>From approximately 500 GB of GMRT data, raw voltages are converted to filterbanks, then de-dispersed and folded across trial dispersion measures.<n>Each candidate yields four diagnostics--summed profile, time vs. phase, subbands vs. phase, and DM curve--represented as arrays and images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pulsar surveys generate millions of candidates per run, overwhelming manual inspection. This thesis builds a deep learning pipeline for radio pulsar candidate selection that fuses array-derived features with image diagnostics. From approximately 500 GB of Giant Metrewave Radio Telescope (GMRT) data, raw voltages are converted to filterbanks (SIGPROC), then de-dispersed and folded across trial dispersion measures (PRESTO) to produce approximately 32,000 candidates. Each candidate yields four diagnostics--summed profile, time vs. phase, subbands vs. phase, and DM curve--represented as arrays and images. A baseline stacked model (ANNs for arrays + CNNs for images with logistic-regression fusion) reaches 68% accuracy. We then refine the CNN architecture and training (regularization, learning-rate scheduling, max-norm constraints) and mitigate class imbalance via targeted augmentation, including a GAN-based generator for the minority class. The enhanced CNN attains 87% accuracy; the final GAN+CNN system achieves 94% accuracy with balanced precision and recall on a held-out test set, while remaining lightweight enough for near--real-time triage. The results show that combining array and image channels improves separability over image-only approaches, and that modest generative augmentation substantially boosts minority (pulsar) recall. The methods are survey-agnostic and extensible to forthcoming high-throughput facilities.

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