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تسجيل مستخدم جديدMachine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys
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Since the start of the Wide Angle Search for Planets (WASP) program, more than 160 transiting exoplanets have been discovered in the WASP data. In the past, possible transit-like events identified by the WASP pipeline have been vetted by human inspection to eliminate false alarms and obvious false positives. The goal of the present paper is to assess the effectiveness of machine learning as a fast, automated, and reliable means of performing the same functions on ground-based wide-field transit-survey data without human intervention. To this end, we have created training and test datasets made up of stellar light curves showing a variety of signal types including planetary transits, eclipsing binaries, variable stars, and non-periodic signals. We use a combination of machine learning methods including Random Forest Classifiers (RFCs) and Convolutional Neural Networks (CNNs) to distinguish between the different types of signals. The final algorithms correctly identify planets in the test data ~90% of the time, although each method on its own has a significant fraction of false positives. We find that in practice, a combination of different methods offers the best approach to identifying the most promising exoplanet transit candidates in data from WASP, and by extension similar transit surveys.
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We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit some of thes
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Context. The TESS and PLATO missions are expected to find vast numbers of new transiting planet candidates. However, only a fraction of these candidates will be legitimate planets, and the candidate validation will require a significant amount of fol
low-up resources. Radial velocity follow-up can be carried out only for the most promising candidates around bright, slowly rotating, stars. Thus, before devoting RV resources to candidates, they need to be vetted using cheaper methods, and, in the cases for which an RV confirmation is not feasible, the candidates true nature needs to be determined based on these alternative methods alone. Aims. We study the applicability of multicolour transit photometry in the validation of transiting planet candidates when the candidate signal arises from a real astrophysical source. We seek to answer how securely can we estimate the true uncontaminated star-planet radius ratio when the light curve may contain contamination from unresolved light sources inside the photometry aperture when combining multicolour transit observations with a physics-based contamination model. Methods. The study is based on simulations and ground-based transit observations. The analyses are carried out with a contamination model integrated into the PyTransit v2 transit modelling package, and the observations are carried out with the MuSCAT2 multicolour imager installed in the 1.5 m TCS in the Teide Observatory. Results. We show that multicolour transit photometry can be used to estimate the amount of flux contamination and the true radius ratio. Combining the true radius ratio with an estimate for the stellar radius yields the true absolute radius of the transiting object, which is a valuable quantity in statistical candidate validation, and enough in itself to validate a candidate whose radius falls below the theoretical lower limit for a brown dwarf.
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