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Register a new userLight Curve Analysis of Ground-Based Data from Exoplanets Transit Database
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Photometric observations of exoplanet transits can be used to derive the orbital and physical parameters of an exoplanet. We analyzed several transit light curves of exoplanets that are suitable for ground-based observations whose complete information is available on the Exoplanet Transit Database (ETD). We analyzed transit data of planets including HAT-P-8 b, HAT-P-16 b, HAT-P-21 b, HAT-P-22 b, HAT-P-28 b and HAT-P-30 b using the AstroImageJ (AIJ) software package. In this paper, we investigated 82 transit light curves from ETD, deriving their physical parameters as well as computing their mid-transit times for future Transit Timing Variation (TTV) analyses. The Precise values of the parameters show that using AIJ as a fitting tool for follow-up observations can lead to results comparable to the values at the NASA Exoplanet Archive (the NEA). Such information will be invaluable considering the numbers of future discoveries from the ground and space-based exoplanet surveys.
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GJ 1214b is one of the few known transiting super-Earth-sized exoplanets with a measured mass and radius. It orbits an M-dwarf, only 14.55 pc away, making it a favorable candidate for follow-up studies. However, the composition of GJ 1214bs mysterious atmosphere has yet to be fully unveiled. Our goal is to distinguish between the various proposed atmospheric models to explain the properties of GJ 1214b: hydrogen-rich or hydrogen-He mix, or a heavy molecular weight atmosphere with reflecting high clouds, as latest studies have suggested. Wavelength-dependent planetary radii measurements from the transit depths in the optical/NIR are the best tool to investigate the atmosphere of GJ 1214b. We present here (i) photometric transit observations with a narrow-band filter centered on 2.14 microns and a broad-band I-Bessel filter centered on 0.8665 microns, and (ii) transmission spectroscopy in the H and K atmospheric windows that cover three transits. The obtained photometric and spectrophotometric time series were analyzed with MCMC simulations to measure the planetary radii as a function of wavelength. We determined radii ratios of 0.1173 for I-Bessel and 0.11735 at 2.14 microns. Our measurements indicate a flat transmission spectrum, in agreement with last atmospheric models that favor featureless spectra with clouds and high molecular weight compositions.
Although WASP-14 b is one of the most massive and densest exoplanets on a tight and eccentric orbit, it has never been a target of photometric follow-up monitoring or dedicated observing campaigns. We report on new photometric transit observations of WASP-14 b obtained within the framework of Transit Timing Variations @ Young Exoplanet Transit Initiative (TTV@YETI). We collected 19 light-curves of 13 individual transit events using six telescopes located in five observatories distributed in Europe and Asia. From light curve modelling, we determined the planetary, stellar, and geometrical properties of the system and found them in agreement with the values from the discovery paper. A test of the robustness of the transit times revealed that in case of a non-reproducible transit shape the uncertainties may be underestimated even with a wavelet-based error estimation methods. For the timing analysis we included two publicly available transit times from 2007 and 2009. The long observation period of seven years (2007-2013) allowed us to refine the transit ephemeris. We derived an orbital period 1.2 s longer and 10 times more precise than the one given in the discovery paper. We found no significant periodic signal in the timing-residuals and, hence, no evidence for TTV in the system.
Clouds have been shown to be present in many exoplanetary atmospheres. Cloud formation modeling predicts considerable inhomogeneities of cloud cover, consistent with optical phase curve observations. However, optical phase curves cannot resolve some existing degeneracies between cloud location and cloud optical properties. We present a conceptually simple technique to detect inhomogeneous cloud cover on exoplanets. Such an inhomogeneous cloud cover produces an asymmetric primary transit of the planet in front of the host star. Asymmetric transits produce characteristic residuals compared to a standard symmetric model. Furthermore, bisector spans can be used to determine asymmetries in the transit light curve. We apply a model of asymmetric transits to the light curves of HAT-P-7b, Kepler-7b and HD209458b and search for possible cloud signatures. The nearly uninterrupted Kepler photometry is particularly well-suited for this method since it allows for a very high time resolution. We do not find any statistically sound cloud signature in the data of the considered planets. For HAT-P-7b, a tentative detection of an asymmetric cloud cover is found, consistent with analysis of the optical phase curve. Based on Bayesian probability arguments, a symmetric model with an offset in the transit ephemeris remains, however, the most viable model. Still, this work demonstrates that for suitable targets, namely low-gravity planets around bright stars, the method can be used to constrain cloud cover characteristics and is thus a helpful additional tool to study exoplanetary atmospheres.
In this research, 14 light curves of 10 hot Jupiter exoplanets available on Exoplanet Transit Database (ETD) were analyzed. We extracted the transit parameters using EXOFAST software. Finally, we compared the planets radius parameter calculated by the EXOFAST with the value at the NASA Exoplanet Archive (NEA) using the confidence interval method. According to the results obtained from this comparison, there is an acceptable match for the planets radius with NEA values. Also, based on the average value of 350 mm optics in this study, it shows that the results obtained using small telescopes can be very significant if there is appropriate observational skill to study more discovered planets.
Deep learning techniques have been well explored in the transiting exoplanet field, however previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well-proven object detection framework in the computer vision field. Through training the network on the light curves of the confirmed Kepler exoplanets, our model yields 94% precision and 95% recall for transits with signal-to-noise ratio higher than 6 (set the confidence threshold to 0.6). Giving a slightly lower confidence threshold, recall can reach higher than 97%, which makes our model applicable for large-scale search. We also transfer the trained model to the TESS data and obtain similar performance. The results of our algorithm match the intuition of the human visual perception and make it easy to find single transiting candidates. Moreover, the parameters of the output bounding boxes can also help to find multiplanet systems. Our network and detection functions are implemented in the Deep-Transit toolkit, which is an open-source Python package hosted on GitHub and PyPI.
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