We analyzed four Spitzer/IRAC observations at 3.6 and 4.5 {mu}m of the primary transit of the exoplanet GJ436b, by using blind source separation techniques. These observations are important to investigate the atmospheric composition of the planet GJ4
36b. Previous analyses claimed strong inter-epoch variations of the transit parameters due to stellar variability, casting doubts on the possibility to extract conclusively an atmospheric signal; those analyses also reported discrepant results, hence the necessity of this reanalysis. The method we used has been proposed in Morello et al. (2014) to analyze 3.6 {mu}m transit light-curves of the hot Jupiter HD189733b; it performes an Independent Component Analysis (ICA) on a set of pixel-light-curves, i.e. time series read by individual pixels, from the same photometric observation. Our method only assumes the independence of instrumental and astrophysical signals, and therefore guarantees a higher degree of objectivity compared to parametric detrending techniques published in the literature. The datasets we analyzed in this paper represent a more challenging test compared to the previous ones. Contrary to previous results reported in the literature, our results (1) do not support any detectable inter-epoch variations of orbital and stellar parameters, (2) are photometrically stable at the level 10e-4 in the IR, and (3) the transit depth measurements at the two wavelengths are consistent within 1{sigma}. We also (4) detect a possible transit duration variation (TDV) of 80 s (2 {sigma} significance level), that has not been pointed out in the literature, and (5) confirm no transit timing variations (TTVs) >30 s.
EChOSim is the end-to-end time-domain simulator of the Exoplanet Characterisation Observatory (EChO) space mission. EChOSim has been developed to assess the capability EChO has to detect and characterize the atmospheres of transiting exoplanets, and
through this revolutionize the knowledge we have of the Milky Way and of our place in the Galaxy. Here we discuss the details of the EChOSim implementation and describe the models used to represent the instrument and to simulate the detection. Software simulators have assumed a central role in the design of new instrumentation and in assessing the level of systematics affecting the measurements of existing experiments. Thanks to its high modularity, EChOSim can simulate basic aspects of several existing and proposed spectrometers for exoplanet transits, including instruments on the Hubble Space Telescope and Spitzer, or ground-based and balloon borne experiments. A discussion of different uses of EChOSim is given, including examples of simulations performed to assess the EChO mission.
We present here new transmission spectra of the hot Jupiter HD-189733b using the SpeX instrument on the NASA Infrared Telescope Facility. We obtained two nights of observations where we recorded the primary transit of the planet in the J-, H- and K-b
ands simultaneously, covering a spectral range from 0.94 to 2.42 {mu}m. We used Fourier analysis and other de-trending techniques validated previously on other datasets to clean the data. We tested the statistical significance of our results by calculating the auto-correlation function, and we found that, after the detrending, auto-correlative noise is diminished at most frequencies. Additionally, we repeated our analysis on the out-of-transit data only, showing that the residual telluric contamination is well within the error bars. While these techniques are very efficient when multiple nights of observations are combined together, our results prove that even one good night of observations is enough to provide statistically meaningful data. Our observed spectra are consistent with space-based data recorded in the same wavelength interval by multiple instruments, indicating that ground-based facilities are becoming a viable and complementary option to spaceborne observatories. The best fit to the features in our data was obtained with water vapor. Our error bars are not small enough to address the presence of additional molecules, however by combining the information contained in other datasets with our results, it is possible to explain all the available observations with a modelled atmospheric spectrum containing water vapor, methane, carbon monoxide and hazes/clouds.
