Exoplanet detection using machine learning

Abhishek Malik, Benjamin P. Moster, Christian Obermeier

Monthly Notices of the Royal Astronomical Society 2021 article cited by 36

importance 4/10 ai assisted Stellar, planetary, and galactic studies astronomy stage: research open-source model

Conventional algorithm-based approaches for detecting exoplanets via the transit method needed improvement.

Better transit detection methods expand the ability to discover planets around other stars.

789 features were extracted from each light curve using TSFresh and used to train a LightGBM gradient boosting classifier to detect transit signals.

On K2 campaign 7 data with injected artificial transits, the method was competitive with the box least squares (BLS) method and comparable to state-of-the-art deep learning models (abstract truncated mid-sentence).

Provides a competitive alternative to BLS and deep learning approaches for exoplanet transit searches.

Venue Monthly Notices of the Royal Astronomical Society
Published 2021
Work type article
Authors Abhishek Malik, Benjamin P. Moster, Christian Obermeier
Citations 36 (OpenAlex, at import time)
References 2
Model or method LightGBM gradient boosting with TSFresh features
Organization not stated in source
Primary source https://doi.org/10.1093/mnras/stab3692
Open access green
Keywords exoplanet detection, transit method, LightGBM, TSFresh, light curves, K2 mission
4 / 10

real but narrower advance

Improves exoplanet transit detection methods, aiding astronomical discovery; a useful research tooling advance but no stated impact on millions of lives or an entire industry.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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