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

TL;DR

This paper introduces ExoSpikeNet, a spiking neural network model that analyzes Kepler flux data to improve exoplanet detection accuracy through biologically inspired temporal data processing.

Contribution

It presents a novel SNN architecture specifically designed for exoplanet classification, leveraging temporal flux data from Kepler to enhance detection performance.

Findings

Achieved high accuracy, F1 score, precision, and recall in exoplanet detection.

Demonstrated the effectiveness of SNNs in processing astronomical time-series data.

Outperformed traditional methods in classifying exoplanet signals.

Abstract

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.