Classifying Exoplanet Candidates with Convolutional Neural Networks: Application to the Next Generation Transit Survey

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 data sets 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 light curves can be utilized, while still achieving competitive results. With our best model, we achieve an area under the curve (AUC) score of (95.6±0.2) per cent and an accuracy of (88.5±0.3) per cent on our unseen test data, as well as (76.5±0.4) per cent and (74.6±1.1) per cent 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 data set, 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

WD 0141−675: a case study on how to follow-up astrometric planet candidates around white dwarfs

This work combines spectroscopic and photometric data of the polluted white dwarf WD 0141−675, which has a now retracted astrometric super-Jupiter candidate, and investigates the most promising ways to confirm Gaia astrometric planetary candidates and obtain follow-up data. Obtaining precise radial velocity measurements for white dwarfs is challenging due to their intrinsic faint magnitudes, lack of spectral absorption lines, and broad spectral features. However, dedicated radial velocity campaigns are capable of confirming close-in giant exoplanets (a few MJup) around polluted white dwarfs, where additional metal lines aid radial velocity measurements. Infrared emission from these giant exoplanets is shown to be detectable with JWST Mid-Infrared Instrument (MIRI) and will provide constraints on the formation of the planet. Using the initial Gaia astrometric solution for WD 0141−675 as a case study, if there were a planet with a 33.65 d period or less with a nearly edge-on orbit, (1) ground-based radial velocity monitoring limits the mass to <15.4 MJup, and (2) space-based infrared photometry shows a lack of infrared excess and in a cloud-free planetary cooling scenario, a substellar companion would have to be <16 MJup and be older than 3.7 Gyr. These results demonstrate how radial velocities and infrared photometry can probe the mass of the objects producing some of the astrometric signals, and rule out parts of the brown dwarf and planet mass parameter space. Therefore, combining astrometric data with spectroscopic and photometric data is crucial to both confirm and characterize astrometric planet candidates around white dwarfs

NGTS discovery of a highly inflated Saturn-mass planet and a highly irradiated hot Jupiter

We report the discovery of two new transiting giant exoplanets NGTS-26 b and NGTS-27 b by the Next Generation Transit Survey (NGTS). NGTS-26 b orbits around a G6-type main sequence star every 4.52 days. It has a mass of 0.29-0.06+0.07 MJup and a radius of 1.33-0.05+0.06 RJup making it a Saturn-mass planet with a highly inflated radius. NGTS-27 b orbits around a slightly evolved G3-type star every 3.37 days. It has a mass of 0.59-0.07+0.10 MJup and a radius of 1.40±0.04 RJup, making it a relatively standard hot Jupiter. The transits of these two planetary systems were re-observed and confirmed in photometry by the SAAO 1.0-m telescope, 1.2-m Euler Swiss telescope as well as the TESS spacecraft, and their masses were derived spectroscopically by the CORALIE, FEROS and HARPS spectrographs. Both giant exoplanets are highly irradiated by their host stars and present an anomalously inflated radius, especially NGTS-26 b which is one of the largest objects among peers of similar mass

The Next Generation Transit Survey (NGTS)

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 photometric precision and hence find smaller planets than have previously been detected from the ground. It also operates in red light,maximizing sensitivity to late K and earlyMdwarf stars. The survey specifications call for photometric precision of 0.1 per cent in red light over an instantaneous field of view of 100 deg 2 , 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 20 cm f/2.8 telescopes fitted with back-illuminated deep-depletion CCD cameras is used to survey fields intensively at intermediateGalactic 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 h 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