We present a Python package LDTK that automates the calculation of custom stellar limb darkening (LD) profiles and model-specific limb darkening coefficients (LDC) using the library of PHOENIX-generated specific intensity spectra by Husser et al. (2013). The aim of the package is to facilitate analyses requiring custom generated limb darkening profiles, such as the studies of exoplanet transits-especially transmission spectroscopy, where the transit modelling is carried out for custom narrow passbands-eclipsing binaries (EBs), interferometry, and microlensing events. First, LDTK can be used to compute custom limb darkening profiles with uncertainties propagated from the uncertainties in the stellar parameter estimates. Second, LDTK can be used to estimate the limb-darkening-model specific coefficients with uncertainties for the most common limb-darkening models. Third, LDTK can be directly integrated into the log posterior computation of any pre-existing modelling code with minimal modifications. The last approach can be used to constrain the LD model parameter space directly by the LD profile, allowing for the marginalization over the LD parameter space without the need to approximate the constraint from the LD profile using a prior. Where µ = √ 1 − z 2 = cos γ, z is the normalized distance from the centre of the stellar disk, and γ is the foreshortening angle.

We present k2sc (K2 Systematics Correction), a Python pipeline to model instrumental systematics and astrophysical variability in light curves from the K2 mission. k2sc uses Gaussian process regression to model position-dependent systematics and time-dependent variability simultaneously, enabling the user to remove both (e.g., for transit searches) or to remove systematics while preserving variability (for variability studies). For periodic variables, k2sc automatically computes estimates of the period, amplitude and evolution timescale of the variability. We apply k2sc to publicly available K2 data from campaigns 3-5 showing that we obtain photometric precision approaching that of the original Kepler mission. We compare our results to other publicly available K2 pipelines, showing that we obtain similar or better results, on average. We use transit injection and recovery tests to evaluate the impact of k2sc on planetary transit searches in K2 pdc (Pre-search Data Conditioning) data, for planet-to-star radius ratio down R p /R = 0.01 and periods up to P = 40 d, and show that k2sc significantly improves the ability to distinguish between correct and false detections, particularly for small planets. k2sc can be run automatically on many light curves, or manually tailored for specific objects such as pulsating stars or large amplitude eclipsing binaries. It can be run on ASCII and FITS light curve files, regardless of their origin. Both the code and the processed light curves are publicly available, and we provide instructions for downloading and using them. The methodology used by k2sc will be applicable to future transit search missions such as TESS and PLATO.

We present a fast and user friendly exoplanet transit light curve modelling package PyTransit, implementing optimised versions of the Giménez (2006) and Mandel & Agol (2002) transit models. The package offers an object-oriented Python interface to access the two models implemented natively in Fortran with OpenMP parallelisation. A partial OpenCL version of the quadratic Mandel-Agol model is also included for GPU-accelerated computations. The aim of PyTransit is to facilitate the analysis of photometric time series of exoplanet transits consisting of hundreds of thousands of datapoints, and of multi-passband transit light curves from spectrophotometric observations, as a part of a researchers programming toolkit for building complex, problem-specific, analyses.

We present TRICERATOPS, a new Bayesian tool that can be used to vet and validate TESS Objects of Interest (TOIs). We test the tool on 68 TOIs that have been previously confirmed as planets or rejected as astrophysical false positives. By looking in the false-positive probability (FPP)−nearby false-positive probability (NFPP) plane, we define criteria that TOIs must meet to be classified as validated planets (FPP < 0.015 and NFPP < 10−3), likely planets (FPP < 0.5 and NFPP < 10−3), and likely nearby false positives (NFPP > 10−1). We apply this procedure on 384 unclassified TOIs and statistically validate 12, classify 125 as likely planets, and classify 52 as likely nearby false positives. Of the 12 statistically validated planets, 9 are newly validated. TRICERATOPS is currently the only TESS vetting and validation tool that models transits from nearby contaminant stars in addition to the target star. We therefore encourage use of this tool to prioritize follow-up observations that confirm bona fide planets and identify false positives originating from nearby stars.