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19 April 2024 |
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Article overview
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Unsupervised Method for Correlated Noise Removal for Multi-wavelength Exoplanet Transit Observations | | Ali Dehghan Firoozabadi
; Alejandro Diaz
; Patricio Rojo
; Ismael Soto
; Rodrigo Mahu
; Nestor Becerra Yoma
; Elyar Sedaghati
; | | Date: |
26 Jun 2017 | | 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. | | Source: | arXiv, 1706.8556 | | Services: | Forum | Review | PDF | Favorites | |
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