Related papers: Reliability Correction is Key for Robust Kepler Oc…
In an era when we are charting multiple planets per system, one might wonder the extent to which "missing" (or failing to detect) a planet can skew our interpretation of the system architecture. We address this question with a simple…
A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams. The classical binning and counting…
Currently, we have only limited means to probe the presence of planets at large orbital separations. Foreman-Mackey et al. searched for long-period transiting planets in the Kepler light curves using an automated pipeline. Here, we apply…
We introduce a new method to infer the posterior distribution for planet occurrence rates from radial-velocity (RV) observations. The approach combines posterior samples from the analysis of individual RV datasets of several stars, using…
Recent developments in computational power and machine learning techniques motivate their use in many different astrophysical research areas. Consequently, many machine learning models have been trained to classify exoplanet transit signals…
Data from the Kepler space telescope have led to the discovery of thousands of planet candidates. Most of these candidates are likely to be real exoplanets, but a significant number of false positives still contaminate the sample,…
Even though the original Kepler mission ended due to mechanical failures, the Kepler satellite continues to collect data. Using classification models, we can understand the features exoplanets possess and then use those features to…
Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic…
We present results from high-resolution, optical to near-IR imaging of host stars of Kepler Objects of Interest (KOIs), identified in the original Kepler field. Part of the data were obtained under the Kepler imaging follow-up observation…
The determination of exoplanet properties and occurrence rates using Kepler data critically depends on our knowledge of the fundamental properties (such as temperature, radius and mass) of the observed stars. We present revised stellar…
Over 30% of the ~4000 known exoplanets to date have been discovered using 'validation', where the statistical likelihood of a transit arising from a false positive (FP), non-planetary scenario is calculated. For the large majority of these…
Estimating the marginal likelihoods is an essential feature of model selection in the Bayesian context. It is especially crucial to have good estimates when assessing the number of planets orbiting stars when the models explain the noisy…
We describe the photometric calibration and stellar classification methods used to produce the Kepler Input Catalog (KIC). The KIC is a catalog containing photometric and physical data for sources in the Kepler Mission field of view; it is…
Using the OSIRIS instrument installed on the 10.4-m Gran Telescopio Canarias (GTC) we acquired multi-color transit photometry of four small (Rp < 5 R_Earth) short-period (P < 6 days) planet candidates recently identified by the Kepler space…
The Kepler mission has provided high-accurate photometric data in a long time span for more than two hundred thousands stars, looking for planetary transits. Among the detected candidates, the planetary nature of around 15% has been…
Statistical analyses of large surveys for transiting planets such as the Kepler mission must account for systematic errors and biases. Transit detection depends not only on the planet's radius and orbital period, but also on host star…
New transiting planet candidates are identified in sixteen months (May 2009 - September 2010) of data from the Kepler spacecraft. Nearly five thousand periodic transit-like signals are vetted against astrophysical and instrumental false…
The planet occurrence rate for multiple stars is important in two aspects. First, almost half of stellar systems in the solar neighborhood are multiple systems. Second, the comparison of the planet occurrence rate for multiple stars to that…
We present here the first observationally based determination of the rate of occurrence of circumbinary planets. This is derived from the publicly available Kepler data, using an automated search algorithm and debiasing process to produce…
Binary classification is highly used in credit scoring in the estimation of probability of default. The validation of such predictive models is based both on rank ability, and also on calibration (i.e. how accurately the probabilities…