ExoSpikeNet: A Light Curve Analysis Based Spiking Neural Network for Exoplanet Detection

Exoplanets are celestial bodies orbiting stars beyond our Solar System. Although historically they posed detection challenges, Kepler’s data has revolutionized our understanding.

By analyzing flux values from the Kepler Mission, we investigate the intricate patterns in starlight that may indicate the presence of exoplanets. This study investigates a novel approach for exoplanet classification using Spiking Neural Networks (SNNs) applied to data obtained from the NASA Kepler mission. SNNs offer a unique advantage by mimicking the spiking behavior of neurons in the brain, allowing for more nuanced and biologically inspired processing of temporal data.

Experimental results demonstrate the efficacy of the proposed SNN architecture, excelling in various performance metrics such as accuracy, F1 score, precision, and recall.

Maneet Chatterjee, Anuvab Sen, Subhabrata Roy

Comments: 6 Pages, 10 Figures, 2 Tables, Accepted by the 13th IEEE International Conference on Communication Systems and Network Technologies(CSNT 2024), April 06-07,2024,India
Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM); Earth and Planetary Astrophysics (astro-ph.EP)
Cite as: arXiv:2406.07927 [astro-ph.IM] (or arXiv:2406.07927v1 [astro-ph.IM] for this version)
https://doi.org/10.48550/arXiv.2406.07927
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Journal reference: 2024 13th IEEE International Conference on Communication Systems and Network Technologies (CSNT 2024)
Related DOI:
https://doi.org/10.1109/CSNT60213.2024.10545663
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Submission history
From: Maneet Chatterjee
[v1] Wed, 12 Jun 2024 06:45:45 UTC (528 KB)
https://arxiv.org/abs/2406.07927
Astrobiology