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The Next Generation Transit Survey (NGTS) is a new ground-based sky survey designed to find transiting Neptunes and super-Earths. By covering at least sixteen times the sky area of Kepler we will find small planets around stars that are sufficiently bright for radial velocity confirmation, mass determination and atmospheric characterisation. The NGTS instrument will consist of an array of twelve independently pointed 20cm telescopes fitted with red-sensitive CCD cameras. It will be constructed at the ESO Paranal Observatory, thereby benefiting from the very best photometric conditions as well as follow up synergy with the VLT and E-ELT. Our design has been verified through the operation of two prototype instruments, demonstrating white noise characteristics to sub-mmag photometric precision. Detailed simulations show that about thirty bright super-Earths and up to two hundred Neptunes could be discovered. Our science operations are due to begin in 2014.
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We describe the Next Generation Transit Survey (NGTS), which is a ground-based project searching for transiting exoplanets orbiting bright stars. NGTS builds on the legacy of previous surveys, most notably WASP, and is designed to achieve higher phot
ometric precision and hence find smaller planets than have previously been detected from the ground. It also operates in red light, maximising sensitivity to late K and early M dwarf stars. The survey specifications call for photometric precision of 0.1 per cent in red light over an instantaneous field of view of 100 square degrees, enabling the detection of Neptune-sized exoplanets around Sun-like stars and super-Earths around M dwarfs. The survey is carried out with a purpose-built facility at Cerro Paranal, Chile, which is the premier site of the European Southern Observatory (ESO). An array of twelve 20cm f/2.8 telescopes fitted with back-illuminated deep-depletion CCD cameras are used to survey fields intensively at intermediate Galactic latitudes. The instrument is also ideally suited to ground-based photometric follow-up of exoplanet candidates from space telescopes such as TESS, Gaia and PLATO. We present observations that combine precise autoguiding and the superb observing conditions at Paranal to provide routine photometric precision of 0.1 per cent in 1 hour for stars with I-band magnitudes brighter than 13. We describe the instrument and data analysis methods as well as the status of the survey, which achieved first light in 2015 and began full survey operations in 2016. NGTS data will be made publicly available through the ESO archive.
We report the discovery of four new hot Jupiters with the Next Generation Transit Survey (NGTS). NGTS-15b, NGTS-16b, NGTS-17b, and NGTS-18b are short-period ($P<5$d) planets orbiting G-type main sequence stars, with radii and masses between $1.10-1.30$ $R_J$ and $0.41-0.76$ $M_J$. By considering the host star luminosities and the planets small orbital separations ($0.039-0.052$ AU), we find that all four hot Jupiters are highly irradiated and therefore occupy a region of parameter space in which planetary inflation mechanisms become effective. Comparison with statistical studies and a consideration of the planets high incident fluxes reveals that NGTS-16b, NGTS-17b, and NGTS-18b are indeed likely inflated, although some disparities arise upon analysis with current Bayesian inflationary models. However, the underlying relationships which govern radius inflation remain poorly understood. We postulate that the inclusion of additional hyperparameters to describe latent factors such as heavy element fraction, as well as the addition of an updated catalogue of hot Jupiters, would refine inflationary models, thus furthering our understanding of the physical processes which give rise to inflated planets.
We present the prototype telescope for the Next Generation Transit Survey, which was built in the UK in 2008/09 and tested on La Palma in the Canary Islands in 2010. The goals for the prototype system were severalfold: to determine the level of systematic noise in an NGTS-like system; demonstrate that we can perform photometry at the (sub) millimagnitude level on transit timescales across a wide field; show that it is possible to detect transiting super-Earth and Neptune-sized exoplanets and prove the technical feasibility of the proposed planet survey. We tested the system for around 100 nights and met each of the goals above. Several key areas for improvement were highlighted during the prototyping phase. They have been subsequently addressed in the final NGTS facility which was recently commissioned at ESO Cerro Paranal, Chile.
We present the results of a search for stellar flares in the first data release from the Next Generation Transit Survey (NGTS). We have found 610 flares from 339 stars, with spectral types between F8 and M6, the majority of which belong to the Galactic thin disc. We have used the 13 second cadence NGTS lightcurves to measure flare properties such as the flare amplitude, duration and bolometric energy. We have measured the average flare occurrence rates of K and early to mid M stars and present a generalised method to measure these rates while accounting for changing detection sensitivities. We find that field age K and early M stars show similar flare behaviour, while fully convective M stars exhibit increased white-light flaring activity, which we attribute to their increased spin down time. We have also studied the average flare rates of pre-main sequence K and M stars, showing they exhibit increased flare activity relative to their main sequence counterparts.
Vetting of exoplanet candidates in transit surveys is a manual process, which suffers from a large number of false positives and a lack of consistency. Previous work has shown that Convolutional Neural Networks (CNN) provide an efficient solution to these problems. Here, we apply a CNN to classify planet candidates from the Next Generation Transit Survey (NGTS). For training datasets we compare both real data with injected planetary transits and fully-simulated data, as well as how their different compositions affect network performance. We show that fewer hand labelled lightcurves can be utilised, while still achieving competitive results. With our best model, we achieve an AUC (area under the curve) score of $(95.6pm{0.2})%$ and an accuracy of $(88.5pm{0.3})%$ on our unseen test data, as well as $(76.5pm{0.4})%$ and $(74.6pm{1.1})%$ in comparison to our existing manual classifications. The neural network recovers 13 out of 14 confirmed planets observed by NGTS, with high probability. We use simulated data to show that the overall network performance is resilient to mislabelling of the training dataset, a problem that might arise due to unidentified, low signal-to-noise transits. Using a CNN, the time required for vetting can be reduced by half, while still recovering the vast majority of manually flagged candidates. In addition, we identify many new candidates with high probabilities which were not flagged by human vetters.
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