Bibcode
Schanche, N.; Collier Cameron, A.; Hébrard, G.; Nielsen, L.; Triaud, A. H. M. J.; Almenara, J. M.; Alsubai, K. A.; Anderson, D. R.; Armstrong, D. J.; Barros, S. C. C.; Bouchy, F.; Boumis, P.; Brown, D. J. A.; Faedi, F.; Hay, K.; Hebb, L.; Kiefer, F.; Mancini, L.; Maxted, P. F. L.; Palle, E.; Pollacco, D. L.; Queloz, D.; Smalley, B.; Udry, S.; West, R.; Wheatley, P. J.
Referencia bibliográfica
Monthly Notices of the Royal Astronomical Society, Volume 483, Issue 4, p.5534-5547
Advertised on:
3
2019
Número de citas
38
Número de citas referidas
34
Descripción
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 this 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 data sets 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 per cent 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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