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Register a new userTransdet: a matched-filter based algorithm for transit detection - application to simulated COROT light curves
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We present a matched-filter based algorithm for transit detection and its application to simulated COROT light curves. This algorithm stems from the work by Borde, Rouan & Leger (2003). We describe the different steps we intend to take to discriminate between planets and stellar companions using the three photometric bands provided by COROT. These steps include the search for secondary transits, the search for ellipsoidal variability, and the study of transit chromaticity. We also discuss the performance of this approach in the context of blind tests organized inside the COROT exoplanet consortium.
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The CoRoT space mission was operating for almost 6 years, producing thousands of continuous photometric light curves. The temporal series of exposures are processed by the production pipeline, correcting the data for known instrumental effects. But even after these model-based corrections, some collective trends are still visible in the light curves. We propose here a simple exposure-based algorithm to remove instrumental effects. The effect of each exposure is a function of only two instrumental stellar parameters, position on the CCD and photometric aperture. The effect is not a function of the stellar flux, and therefore much more robust. As an example, we show that the $sim2%$ long-term variation of the early run LRc01 is nicely detrended on average. This systematics removal process is part of the CoRoT legacy data pipeline.
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.
When a planet occults a spotty area on a stellar surface, the flux increases and a characteristic feature in a light curve - a bump - is observed. Among the planets detected by the CoRoT-mission CoRoT-18 is especially interesting as it exhibited spot crossings that we have analysed in detail. We used four ground-based observations obtained at a 1.5-m telescope in Spain and the 13 available CoRoT-transits to refine and constrain stellar, planetary and geometrical parameters of the system. We found that the derived physical properties slightly deviate from the previously published values, most likely due to the different treatment of the stellar activity. Following a spot over several transits enabled us to measure the stellar rotation period and the spin-orbit alignment. Our derived values of Prot=5.19 +/- 0.03 d and Lambda=6 +/- 13 deg are in agreement with the literature values that were obtained with other methods. Although we cannot exclude a very old age for CoRoT-18, our observations support the young star hypothesis and, hence, yield constraints on the time-scale of planet formation and migration.
Context. Transit detection algorithms are mathematical tools used for detecting planets in the photometric data of transit surveys. In this work we study their application to space-based surveys. Aims: Space missions are exploring the parameter space of the transit surveys where classical algorithms do not perform optimally, either because of the challenging signal-to-noise ratio of the signal or its non-periodic characteristics. We have developed an algorithm addressing these challenges for the mission CoRoT. Here we extend the application to the data from the space mission Kepler. We aim at understanding the performances of algorithms in different data sets. Methods: We built a simple analytical model of the transit signal and developed a strategy for the search that improves the detection performance for transiting planets. We analyzed Kepler data with a set of stellar activity filtering and transit detection tools from the CoRoT community that are designed for the search of transiting planets. Results: We present a new algorithm and its performances compared to one of the most widely used techniques in the literature using CoRoT data. Additionally, we analyzed Kepler data corresponding to quarter Q1 and compare our results with the most recent list of planetary candidates from the Kepler survey. We found candidates that went unnoticed by the Kepler team when analyzing longer data sets. We study the impact of instrumental features on the production of false alarms and false positives. These results show that the analysis of space mission data advocates the use of complementary detrending and transit detection tools also for future space-based transit surveys such as PLATO.
Strong variations of any kind and causes within a stellar light curve may prohibit the detection of transits, particularly of faint or shallow transits caused by small planets passing in front of the stellar disk. The success of future space telescopes with the goal for finding small planets will be based on proper filtering, analysis and detection of transits in perturbed stellar light curves. The wavelet-based filter methods VARLET and PHALET, developed by RIU-PF, in combination with the transit detection software package EXOTRANS allow the extraction of (i) strong stellar variations, (ii) instrument caused spikes and singularities within a stellar light curve, (iii) already detected planetary or stellar binary transits in order to be able to search for further planets or planets about binary stars. Once the light curve is filtered, EXOTRANS is able to search efficiently, effectively and precisely for transits, in particular for faint transits.
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