Transit clairvoyance: Enhancing TESS follow-up using artificial neural networks
Monthly Notices of the Royal Astronomical Society, ISSN: 1365-2966, Vol: 465, Issue: 3, Page: 3495-3505
2017
- 17Citations
- 25Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The upcoming Transiting Exoplanet Survey Satellite (TESS) mission is expected to find thousands of transiting planets around bright stars, yet for three-quarters of the fields observed the temporal coverage will limit discoveries to planets with orbital periods below 13.7 d. From the Kepler catalogue, the mean probability of these short-period transiting planets having additional longer period transiters (which would be missed by TESS) is 18 per cent, a value 10 times higher than the average star. In this work, we show how this probability is not uniform but functionally dependent upon the properties of the observed short-period transiters, ranging from less than 1 per cent up to over 50 per cent. Using artificial neural networks (ANNs) trained on the Kepler catalogue and making careful feature selection to account for the differing sensitivity of TESS, we are able to predict the most likely short-period transiters to be accompanied by additional transiters. Through cross-validation, we predict that a targeted, optimized TESS transit and/or radial velocity follow-up programme using our trained ANN would have a discovery yield improved by a factor of 2. Our work enables a near-optimal follow-up strategy for surveys following TESS targets for additional planets, improving the science yield derived from TESS and particularly beneficial in the search for habitable-zone transiting worlds.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85014662872&origin=inward; http://dx.doi.org/10.1093/mnras/stw2974; https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stw2974; https://dx.doi.org/10.1093/mnras/stw2974; https://academic.oup.com/mnras/article/465/3/3495/2627194
Oxford University Press (OUP)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know