Automatic vetting of planet candidates from ground based surveys : machine learning with NGTS

State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context

NGTS-2b: an inflated hot-Jupiter transiting a bright F-dwarf

We report the discovery of NGTS-2b, an inflated hot-Jupiter transiting a bright F5V star (2MASS J14202949 - 3112074; Teff = 6478^{+94}_{-89} K), discovered as part of the Next Generation Transit Survey (NGTS). The planet is in a P = 4.51 d orbit with mass 0.74^{+0.13}_{-0.12}MJ, radius 1.595^{+0.047}_{-0.045}RJ, and density 0.226^{+0.040}_{-0.038} g cm-3; therefore one of the lowest density exoplanets currently known. With a relatively deep 1.0{{ per cent}} transit around a bright V = 10.96 host star, NGTS-2b is a prime target for probing giant planet composition via atmospheric transmission spectroscopy. The rapid rotation (v sin i = 15.2 ± 0.8 km s-1) also makes this system an excellent candidate for Rossiter-McLaughlin follow-up observations, to measure the sky-projected stellar obliquity. NGTS-2b was confirmed without the need for follow-up photometry, due to the high precision of the NGTS photometry

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article