Abstract:
In this work, we investigate the use of DM-time images (dispersion measurement) as input for convolutional neural networks (CNNs) to classify pulsar and transient radio signals. Our previous work highlighted significant limitations with spectrogram-based models, particularly low sensitivity in detecting faint pulses amidst noise. The decision was made to use DM-time images, which capture detailed dispersion characteristics, offering enhanced detection capabilities for weak signals. We developed minimalist CNN architectures, ranging from one to three convolutional layers, optimized for real-time processing with reduced computational demands. The models were trained and tested using datasets derived from Crab Pulsar observations, with promising results demonstrating robust pulse detection even under challenging signal-to-noise conditions. The sensitivity of the models was evaluated against both real and synthetic data, showing high accuracy for pulses with SNR greater than 7. Furthermore, performance tests on a high-performance cluster revealed the feasibility of using these models in real-time applications, with scalable improvements in execution time as CPU resources were increased. This work provides a foundation for efficient and scalable real-time pulse classification in radio astronomy.
