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Register a new userTransit Shapes and Self Organising Maps as a Tool for Ranking Planetary Candidates: Application to Kepler and K2
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A crucial step in planet hunting surveys is to select the best candidates for follow up observations, given limited telescope resources. This is often performed by human `eyeballing, a time consuming and statistically awkward process. Here we present a new, fast machine learning technique to separate true planet signals from astrophysical false positives. We use Self Organising Maps (SOMs) to study the transit shapes of emph{Kepler} and emph{K2} known and candidate planets. We find that SOMs are capable of distinguishing known planets from known false positives with a success rate of 87.0%, using the transit shape alone. Furthermore, they do not require any candidates to be dispositioned prior to use, meaning that they can be used early in a missions lifetime. A method for classifying candidates using a SOM is developed, and applied to previously unclassified members of the emph{Kepler} KOI list as well as candidates from the emph{K2} mission. The method is extremely fast, taking minutes to run the entire KOI list on a typical laptop. We make texttt{Python} code for performing classifications publicly available, using either new SOMs or those created in this work. The SOM technique represents a novel method for ranking planetary candidate lists, and can be used both alone or as part of a larger autovetting code.
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Accurate photometric redshift calibration is central to the robustness of all cosmology constraints from cosmic shear surveys. Analyses of the KiDS re-weighted training samples from all overlapping spectroscopic surveys to provide a direct redshift calibration. Using self-organising maps (SOMs) we demonstrate that this spectroscopic compilation is sufficiently complete for KiDS, representing $99%$ of the effective 2D cosmic shear sample. We use the SOM to define a $100%$ represented `gold cosmic shear sample, per tomographic bin. Using mock simulations of KiDS and the spectroscopic training set, we estimate the uncertainty on the SOM redshift calibration, and find that photometric noise, sample variance, and spectroscopic selection effects (including redshift and magnitude incompleteness) induce a combined maximal scatter on the bias of the redshift distribution reconstruction ($Delta langle z rangle=langle z rangle_{rm est}-langle z rangle_{rm true}$) of $sigma_{Delta langle z rangle} leq 0.006$ in all tomographic bins. We show that the SOM calibration is unbiased in the cases of noiseless photometry and perfectly representative spectroscopic datasets, as expected from theory. The inclusion of both photometric noise and spectroscopic selection effects in our mock data introduces a maximal bias of $Delta langle z rangle =0.013pm0.006$, or $Delta langle z rangle leq 0.025$ at $97.5%$ confidence, once quality flags have been applied to the SOM. The method presented here represents a significant improvement over the previously adopted direct redshift calibration implementation for KiDS, owing to its diagnostic and quality assurance capabilities. The implementation of this method in future cosmic shear studies will allow better diagnosis, examination, and mitigation of systematic biases in photometric redshift calibration.
Photometric surveys such as Kepler have the precision to identify exoplanet and eclipsing binary candidates from only a single transit. K2, with its 75d campaign duration, is ideally suited to detect significant numbers of single-eclipsing objects. Here we develop a Bayesian transit-fitting tool (Namaste: An Mcmc Analysis of Single Transit Exoplanets) to extract orbital information from single transit events. We achieve favourable results testing this technique on known Kepler planets, and apply the technique to 7 candidates identified from a targeted search of K2 campaigns 1, 2 and 3. We find EPIC203311200 to host an excellent exoplanet candidate with a period, assuming zero eccentricity, of $540 ^{+410}_{-230}$ days and a radius of $0.51 pm 0.05 R_{Jup}$. We also find six further transit candidates for which more follow-up is required to determine a planetary origin. Such a technique could be used in the future with TESS, PLATO and ground-based photometric surveys such as NGTS, potentially allowing the detection of planets in reach of confirmation by Gaia.
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.
We produce light curves for all ~34,000 targets observed with K2 in Campaign 17 (C17), identifying 34 planet candidates, 184 eclipsing binaries, and 222 other periodic variables. The location of the C17 field means follow-up can begin immediately now that the campaign has concluded and interesting targets have been identified. The C17 field has a large overlap with C6, so this latest campaign also offers a rare opportunity to study a large number of targets already observed in a previous K2 campaign. The timing of the C17 data release, shortly before science operations begin with the Transiting Exoplanet Survey Satellite (TESS), also lets us exercise some of the tools and methods developed for identification and dissemination of planet candidates from TESS. We find excellent agreement between these results and those identified using only K2-based tools. Among our planet candidates are several planet candidates with sizes < 4 R_E and orbiting stars with KepMag < 10 (indicating good RV targets of the sort TESS hopes to find) and a Jupiter-sized single-transit event around a star already hosting a 6 d planet candidate.
Given that Campaign 16 of the K2 mission is one of just two K2 campaigns observed so far in forward-facing mode, which enables immediate follow-up observations from the ground, we present a catalog of interesting targets identified through photometry alone. Our catalog includes 30 high-quality planet candidates (showing no signs of being non-planetary in nature), 48 more ambiguous events that may be either planets or false positives, 164 eclipsing binaries, and 231 other regularly periodic variable sources. We have released light curves for all targets in C16, and have also released system parameters and transit vetting plots for all interesting candidates identified in this paper. Of particular interest is a candidate planet orbiting the bright F dwarf HD 73344 (V=6.9, K=5.6) with an orbital period of 15 days. If confirmed, this object would correspond to a $2.56 pm 0.18 R_oplus$ planet and would likely be a favorable target for radial velocity characterization. This paper is intended as a rapid release of planet candidates, eclipsing binaries and other interesting periodic variables to maximize the scientific yield of this campaign, and as a test run for the upcoming TESS mission, whose frequent data releases call for similarly rapid candidate identification and efficient follow-up.
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