Exoplanetology: Exoplanets & Exomoons

Advancing Machine Learning for Stellar Activity and Exoplanet Period Rotation

By Keith Cowing

Status Report

astro-ph.SR

September 10, 2024

Filed under AI, astro-ph.SR, astronomy, atmosphere, exoplanet, Kepler, Machine learning

Advancing Machine Learning for Stellar Activity and Exoplanet Period Rotation — The Lomb-Scargle periodograms computed from the photometric light curves of the exoplanet host stars in our dataset. The periodograms identify several statistically significant periodic signals present in the data, with the strongest peak (gray shaded line) in each plot corresponding to the estimated stellar rotation period. To quantify the uncertainty in these period estimates, we have fitted a Gaussian function to the primary peak and determined the half-width at half-maximum (HWHM) of the Gaussian (red-dashed line). This HWHM value provides an estimate of the period uncertainty that accounts for the sampling characteristics of the periodogram. — astro-ph.SR

This study applied machine learning models to estimate stellar rotation periods from corrected light curve data obtained by the NASA Kepler mission. Traditional methods often struggle to estimate rotation periods accurately due to noise and variability in the light curve data.

The workflow involved using initial period estimates from the LS-Periodogram and Transit Least Squares techniques, followed by splitting the data into training, validation, and testing sets. We employed several machine learning algorithms, including Decision Tree, Random Forest, K-Nearest Neighbors, and Gradient Boosting, and also utilized a Voting Ensemble approach to improve prediction accuracy and robustness.

The analysis included data from multiple Kepler IDs, providing detailed metrics on orbital periods and planet radii. Performance evaluation showed that the Voting Ensemble model yielded the most accurate results, with an RMSE approximately 50% lower than the Decision Tree model and 17% better than the K-Nearest Neighbors model.

The Random Forest model performed comparably to the Voting Ensemble, indicating high accuracy. In contrast, the Gradient Boosting model exhibited a worse RMSE compared to the other approaches. Comparisons of the predicted rotation periods to the photometric reference periods showed close alignment, suggesting the machine learning models achieved high prediction accuracy.

The results indicate that machine learning, particularly ensemble methods, can effectively solve the problem of accurately estimating stellar rotation periods, with significant implications for advancing the study of exoplanets and stellar astrophysics.

Fatemeh Fazel Hesar, Bernard Foing, Ana M. Heras, Mojtaba Raouf, Victoria Foing, Shima Javanmardi, Fons J. Verbeek

Comments: 15 pages, 8 figures. Submitted for publication in A&A
Subjects: Solar and Stellar Astrophysics (astro-ph.SR); Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2409.05482 [astro-ph.SR] (or arXiv:2409.05482v1 [astro-ph.SR] for this version)
https://doi.org/10.48550/arXiv.2409.0548 2
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Submission history
From: Fatemeh Fazel Hesar
[v1] Mon, 9 Sep 2024 10:25:13 UTC (2,436 KB)
https://arxiv.org/abs/2409.05482
Astrobiology

Keith Cowing

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, Na’Vi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him) 🖖🏻

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