Unsupervised Method for Correlated Noise Removal for Multi-wavelength Exoplanet Transit Observations. (30th May 2017)

• Record Type: Journal Article
• Authors: Dehghan Firoozabadi, Ali; Diaz, Alejandro; Rojo, Patricio; Soto, Ismael; Mahu, Rodrigo; Yoma, Nestor Becerra; Sedaghati, Elyar
• Is Part Of: Publications of the Astronomical Society of the Pacific. Volume 129:Number 977(2017)
• Publication Date: 2017-05-30
• Subjects: planet–star interactions -- stars: atmospheres -- (stars:) planetary systems; Astronomy -- Periodicals; Astronomy; Periodicals; Periodicals; 520.5
• DOI: 10.1088/1538-3873/aa70df
• Languages: English
• ISSNs: 0004-6280

Abstract

Abstract: Exoplanetary atmospheric observations require an exquisite precision in the measurement of the relative flux among wavelengths. In this paper, we aim to provide a new adaptive method to treat light curves before fitting transit parameters in order to minimize systematic effects that affect, for instance, ground-based observations of exo-atmospheres. We propose a neural-network-based method that uses a reference built from the data itself with parameters that are chosen in an unsupervised fashion. To improve the performance of proposed method, K-means clustering and Silhouette criteria are used for identifying similar wavelengths in each cluster. We also constrain under which circumstances our method improves the measurement of planetary-to-stellar radius ratio without producing significant systematic offset. We tested our method in high quality data from WASP-19b and low-quality data from GJ-1214. We succeed in providing smaller error bars for the former when using JKTEBOP, but GJ-1214 light curve was beyond the capabilities of this method to improve as it was expected from our validation tests.