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Deep learning for pulsar detection: Investigating hyperparameter effects on TensorFlow classification accuracy

Upadhyay et al. | Jan 31, 2026

Deep learning for pulsar detection: Investigating hyperparameter effects on TensorFlow classification accuracy

This study investigates how the hyperparameters epochs and batch size affect the classification accuracy of a convolutional neural network (CNN) trained on pulsar candidate data. Our results reveal that accuracy improves with increasing number of epochs and smaller batch sizes, suggesting that with optimized hyperparameters, high accuracy may be achievable with minimal training. These findings offer insights that could help create more efficient machine learning classification models for pulsar signal detection, with the potential of accelerating pulsar discovery and advancing astrophysical research.

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Using Gravitational Waves to Determine if Primordial Black Holes are Sources of Dark Matter

Sivakumar et al. | Jul 15, 2024

Using Gravitational Waves to Determine if Primordial Black Holes are Sources of Dark Matter

In the quest to understand dark matter, scientists face a profound mystery. Two compelling candidates, Massive Compact Halo Objects (MACHOs) and Weakly Interacting Massive Particles (WIMPs), have emerged as potential sources. By analyzing gravitational waves from binary mergers involving these black holes, authors sought to determine if MACHOs could be the elusive dark matter.

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