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The precise derivation of transit depths from transit light curves is a keycomponent for measuring exoplanet transit spectra, and henceforth for the studyof exoplanet atmospheres. However, it is still deeply affected by various kindsof systematic errors and noise. In this paper we propose a new detrendingmethod by reconstructing the stellar flux baseline during transit time. Wetrain a probabilistic Long Short-Term Memory (LSTM) network to predict the nextdata point of the light curve during the out-of-transit, and use this model toreconstruct a transit-free light curve - i.e. including only the systematics -during the in-transit. By making no assumption about the instrument, and usingonly the transit ephemeris, this provides a general way to correct thesystematics and perform a subsequent transit fit. The name of the proposedmodel is TLCD-LSTM, standing for Transit Light Curve Detrending LSTM. Here wepresent the first results on data from six transit observations of HD 189733bwith the IRAC camera on board the Spitzer Space Telescope, and discuss some ofits possible further applications.

Detrending Exoplanetary Transit Light Curves with Long Short-Term Memory Networks