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Kepler Light Curve Classification Using Deep Learning and Markov Transition Field (Student Abstract)

AAAI 2024 · Time series › Foundation models for time series · approach: Satellite image time series · 2 citations (OpenAlex)

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Converts Kepler light curves into Markov Transition Field images and classifies them as exoplanet vs false positive with a custom CNN, reporting 99.39% accuracy versus standard-plot and GADF baselines.

Problem

Manually classifying Kepler light curves as exoplanet or false positive is infeasible at large data volumes, so an automated classifier is needed.

Approach

Encode each light curve time series as a Markov Transition Field image so periodic dimming becomes visible as structure, then classify the image with a small custom CNN instead of feeding raw 1D light curves to models such as AstroNet.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Convert light curves to MTF images instead of standard light-curve plotsKepler light-curve classifier · Kepler KOI holdoutaccuracy: 93.35 -> 99.39Helped
Convert light curves to MTF images instead of GADF imagesKepler light-curve classifier · Kepler KOI holdoutaccuracy: 94.06 -> 99.39Helped

Surprising findings

Limitations

Experimental setup

Datasets: NASA Exoplanet Archive Kepler light curves (6552 KOIs)

Benchmarks: Kepler KOI light curve classification (held-out 10% split)

Metrics: accuracy precision recall F-score

Baselines: Standard light curve plot CNN GADF CNN AstroNet (reported 95.8%) Dattilo et al. 2019 modified AstroNet (reported 98%)

Builds on: AstroNet Markov Transition Field Gramian Angular Field

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