Comparing Classification Models on Kepler Data

TL;DR

This paper compares various classification models on Kepler data to identify exoplanet candidates, using statistical tests to determine significance and improve detection efficiency.

Contribution

It introduces a systematic comparison of classification models on Kepler data, highlighting feature importance and model significance for exoplanet candidate detection.

Findings

Model comparison results highlight the most effective classifiers.

Features that distinguish exoplanet candidates from false positives are identified.

Statistical significance of model differences is confirmed using McNemar's test.

Abstract

Even though the original Kepler mission ended due to mechanical failures, the Kepler satellite continues to collect data. Using classification models, we can understand the features exoplanets possess and then use those features to investigate further for any more information on the candidate planet. Based on the classification model, the idea is to find out the probability of the planet under observation being a candidate for an exoplanet or a false positive. If the model predicts that the observation is a candidate for being an exoplanet, then the further investigation can be conducted. From the model, we can narrow down the features that might explain the difference between a candidate and a false-positive which ultimately helps us to increase the efficiency of any model and fine-tune the model and ultimately the process of searching for any future exoplanets. The model comparison is supported by McNemar's test for checking significance.