The Giants pipeline accesses TESS data, produces noise-corrected light curves, and searches for planets transiting evolved stars. Built with Lightkurve (ascl:1812.013) and written in Python, its emphasis is on finding giant planets around subgiant and RGB stars in TESS Full Frame Images (FFIs). Giants produces a one-page PDF summary for each target.

ld-exosim selects the optimal (i.e. best estimator in a MSE sense) limb-darkening law for a given transiting exoplanet lightcurve and calculates the limb-darkening induced biases on various exoplanet parameters. Limb-darkening laws include linear, quadratic, logarithmic, square-root and three-parameter laws.

MC-SPAM (Monte-Carlo Synthetic-Photometry/Atmosphere-Model) generates limb-darkening coefficients from models that are comparable to transit photometry; it extends the original SPAM algorithm by Howarth (2011) by taking in consideration the uncertainty on the stellar and transit parameters of the system under analysis.

The Collection of Extraction Routines for Echelle Spectra (CERES) constructs automated pipelines for the reduction, extraction, and analysis of echelle spectrograph data. This modular code includes tools for handling the different steps of the processing: CCD reductions, tracing of the echelle orders, optimal and simple extraction, computation of the wave-length solution, estimation of radial velocities, and rough and fast estimation of the atmospheric parameters. The standard output of pipelines constructed with CERES is a FITS cube with the optimally extracted, wavelength calibrated and instrumental drift-corrected spectrum for each of the science images. Additionally, CERES includes routines for the computation of precise radial velocities and bisector spans via the cross-correlation method, and an automated algorithm to obtain an estimate of the atmospheric parameters of the observed star.