Authors
Massimiliano Giordano Orsini, Alessio Ferone, Laura Inno, Paolo Giacobbe, Antonio Maratea, Angelo Ciaramella, Aldo Stefano Bonomo, Alessandra Rotundi
Journal
Abstract
Ground-based high-resolution transmission spectroscopy has become a critical tool for probing the chemical compositions of transiting exoplanetary atmospheres. A well-known challenge in this scope lies in the detrending process, which consists in effectively removing contaminating stellar and telluric absorption features obscuring the planetary spectrum. Principal Component Analysis (PCA) is the current state-of-the-art method, but its effectiveness depends on selecting the correct number of components—a subjective choice that impacts how much of the planetary signal is preserved or lost, and the features to be removed are well represented by the linear combination of the principal components. Additionally, there is no quantitative framework for distinguishing between residuals from incomplete subtraction and those containing the true planetary signal. In this work, we introduce a novel, computer vision-inspired approach to the task of detrending using Deep Convolutional Generative Adversarial Networks (DCGANs), combined with semantic image inpainting, able to overcome the limitations of PCA. In contrast to PCA, our proposed detrending method operates in a non-linear fashion, allowing for a scalable and robust separation of planetary atmospheric features from interfering signals and eliminating reliance on the manual selection of principal components. As a case study, we consider observations of the ultra-hot Jupiter KELT-9 b acquired by the HARPS-N spectrograph at the Telescopio Nazionale Galileo. Although further refinement is needed for full competitiveness with PCA, our method successfully produces realistic transit-free nights and promising residuals, paving the way for future machine learning-driven detrending methods.
Description
The article “A Data-Driven Approach for Extracting Exoplanetary Atmospheric Features” introduces a novel machine learning method to enhance the detection of chemical signatures in exoplanetary atmospheres.
Traditionally, Principal Component Analysis (PCA) has been used to remove unwanted stellar and telluric signals from spectroscopic observations, but its performance is limited by subjectivity and linearity. This study proposes a nonlinear detrending framework based on Deep Convolutional Generative Adversarial Networks (DCGANs) combined with semantic image inpainting, enabling more robust separation of planetary features from contaminating signals.
Tested on observations of the ultra-hot Jupiter KELT-9 b acquired with the HARPS-N spectrograph, the technique demonstrates promising results and lays the groundwork for fully data-driven approaches to exoplanet atmosphere retrieval.