  The precise derivation of transit depths from transit light curves is a key
component for measuring exoplanet transit spectra, and henceforth for the study
of exoplanet atmospheres. However, it is still deeply affected by various kinds
of systematic errors and noise. In this paper we propose a new detrending
method by reconstructing the stellar flux baseline during transit time. We
train a probabilistic Long Short-Term Memory (LSTM) network to predict the next
data point of the light curve during the out-of-transit, and use this model to
reconstruct a transit-free light curve - i.e. including only the systematics -
during the in-transit. By making no assumption about the instrument, and using
only the transit ephemeris, this provides a general way to correct the
systematics and perform a subsequent transit fit. The name of the proposed
model is TLCD-LSTM, standing for Transit Light Curve Detrending LSTM. Here we
present the first results on data from six transit observations of HD 189733b
with the IRAC camera on board the Spitzer Space Telescope, and discuss some of
its possible further applications.
