Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost

Fiscale, Stefano; Ferone, Alessio; Ciaramella, Angelo; Inno, Laura; Giordano Orsini, Massimiliano; Covone, Giovanni; Rotundi, Alessandra

doi:10.3390/electronics14091738

Open AccessArticle

Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost

by

Stefano Fiscale

^1,2,*

,

Alessio Ferone

³

,

Angelo Ciaramella

³

,

Laura Inno

^1,2,3

,

Massimiliano Giordano Orsini

¹

,

Giovanni Covone

^2,4,5

and

Alessandra Rotundi

^1,3

¹

UNESCO Chair “Environment, Resources and Sustainable Development”, Department of Science and Technology, Parthenope University of Naples, 80133 Naples, Italy

²

Istituto Nazionale di Astrofisica, Osservatorio Astronomico di Capodimonte, Salita Moiariello, 16, 80131 Naples, Italy

³

Department of Science and Technology, Centro Direzionale di Napoli, Parthenope University of Naples, 80143 Naples, Italy

⁴

Department of Physics “Ettore Pancini”, University of Naples Federico II, 80138 Naples, Italy

⁵

INFN Section of Naples, Via Cinthia 6, 80126 Naples, Italy

^*

Author to whom correspondence should be addressed.

Electronics 2025, 14(9), 1738; https://doi.org/10.3390/electronics14091738

Submission received: 7 March 2025 / Revised: 19 April 2025 / Accepted: 21 April 2025 / Published: 24 April 2025

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network: 2nd Edition)

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Abstract

NASA’s space-based telescopes Kepler and Transiting Exoplanet Survey Satellite (TESS) have detected billions of potential planetary signatures, typically classified with Convolutional Neural Networks (CNNs). In this study, we introduce a hybrid model that combines deep learning, dimensionality reduction, decision trees, and diffusion models to distinguish planetary transits from astrophysical false positives and instrumental artifacts. Our model consists of three main components: (i) feature extraction using the CNN VGG19, (ii) dimensionality reduction through t-Distributed Stochastic Neighbor Embedding (t-SNE), and (iii) classification using Conditional Flow Matching (CFM) and XGBoost. We evaluated the model on two Kepler and one TESS datasets, achieving F1-scores of 98% and 100%, respectively. Our results demonstrate the effectiveness of VGG19 in extracting discriminative patterns from data, t-SNE in projecting features in a lower dimensional space where they can be most effectively classified, and CFM with XGBoost in enabling robust classification with minimal computational cost. This study highlights that a hybrid approach leveraging deep learning and dimensionality reduction allows one to achieve state-of-the-art performance in exoplanet detection while maintaining a low computational cost. Future work will explore the use of adaptive dimensionality reduction methods and the application to data from upcoming missions like the ESA’s PLATO mission.

Keywords: exoplanet detection; deep learning; dimensionality reduction; diffusion models; decision trees

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MDPI and ACS Style

Fiscale, S.; Ferone, A.; Ciaramella, A.; Inno, L.; Giordano Orsini, M.; Covone, G.; Rotundi, A. Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost. Electronics 2025, 14, 1738. https://doi.org/10.3390/electronics14091738

AMA Style

Fiscale S, Ferone A, Ciaramella A, Inno L, Giordano Orsini M, Covone G, Rotundi A. Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost. Electronics. 2025; 14(9):1738. https://doi.org/10.3390/electronics14091738

Chicago/Turabian Style

Fiscale, Stefano, Alessio Ferone, Angelo Ciaramella, Laura Inno, Massimiliano Giordano Orsini, Giovanni Covone, and Alessandra Rotundi. 2025. "Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost" Electronics 14, no. 9: 1738. https://doi.org/10.3390/electronics14091738

APA Style

Fiscale, S., Ferone, A., Ciaramella, A., Inno, L., Giordano Orsini, M., Covone, G., & Rotundi, A. (2025). Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost. Electronics, 14(9), 1738. https://doi.org/10.3390/electronics14091738

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Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost

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