摘要：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.
关键词：Exoplanet atmospheres;Photometric systems;Astronomy data analysis;Extrasolar gas giants;Neural networks
