A list of keywords associated with codes in the ASCL.NASA (169), Kepler (31), Spitzer (13), TESS (13), Fermi (6), HITS (6), HST (5), ROSAT (4), Swift (4), CGRO (3), LISA (3), RXTE (3), ASCA (2), Chandra (2), COBE (2), Geotail (2), Heliophysics (2), Herschel (2), LRO (2), Magellan (2), MRO (2), NICER (2), Polar (2), Rosetta (2), Wind (2), WISE (2), WMAP (2), Apollo (1), Cassini (1), Dawn (1), GOES (1), Hinode (1), Hitomi (1), InSight (1), INTEGRAL (1), ISO (1), Juno (1), JWST (1), K2 (1), Lucy (1), Lunar Quest (1), MAVEN (1), MESSENGER (1), MGS (1), NEAR (1), New Horizons (1), NISAR (1), NuSTAR (1), OSIRIS-REx (1), Parker Solar Probe (1), Psyche (1), RHESSI (1), SDO (1), SOFIA (1), SOHO (1), STEREO (1), Suzaku (1), THEMIS (1), TRMM (1)
Transit Clairvoyance uses Artificial Neural Networks (ANNs) to predict the most likely short period transiters to have additional transiters, which may double the discovery yield of the TESS (Transiting Exoplanet Survey Satellite). Clairvoyance is a simple 2-D interpolant that takes in the number of planets in a system with period less than 13.7 days, as well as the maximum radius amongst them (in Earth radii) and orbital period of the planet with maximum radius (in Earth days) in order to predict the probability of additional transiters in this system with period greater than 13.7 days.
EVEREST (EPIC Variability Extraction and Removal for Exoplanet Science Targets) removes instrumental noise from light curves with pixel level decorrelation and Gaussian processes. The code, written in Python, generates the EVEREST catalog and offers tools for accessing and interacting with the de-trended light curves. EVEREST exploits correlations across the pixels on the CCD to remove systematics introduced by the spacecraft’s pointing error. For K2, it yields light curves with precision comparable to that of the original Kepler mission. Interaction with the EVEREST catalog catalog is available via the command line and through the Python interface. Though written for K2, EVEREST can be applied to additional surveys, such as the TESS mission, to correct for instrumental systematics and enable the detection of low signal-to-noise transiting exoplanets.
Lightkurve analyzes astronomical flux time series data, in particular the pixels and light curves obtained by NASA’s Kepler, K2, and TESS exoplanet missions. This community-developed Python package is designed to be user friendly to lower the barrier for students, astronomers, and citizen scientists interested in analyzing data from these missions. Lightkurve provides easy tools to download, inspect, and analyze time series data and its documentation is supported by a large syllabus of tutorials.
eleanor extracts target pixel files from TESS Full Frame Images and produces systematics-corrected light curves for any star observed by the TESS mission. eleanor takes a TIC ID, a Gaia source ID, or (RA, Dec) coordinates of a star observed by TESS and returns, as a single object, a light curve and accompanying target pixel data. The process can be customized, allowing, for example, examination of intermediate data products and changing the aperture used for light curve extraction. eleanor also offers tools that make it easier to work with stars observed in multiple TESS sectors.
sbpy, an Astropy affiliated package, supplements functionality provided by Astropy (ascl:1304.002) with functions and methods that are frequently used for planetary astronomy with a clear focus on asteroids and comets. It offers access tools for various databases for orbital and physical data, spectroscopy analysis tools and models, photometry models for resolved and unresolved observations, ephemerides services, and other tools useful for small-body planetary astronomy.
dips detrends timeseries of strictly periodic signals. It does not assume any functional form for the signal or the background or the noise; it disentangles the strictly periodic component from everything else. It has been used for detrending Kepler, K2 and TESS timeseries of periodic variable stars, eclipsing binary stars, and exoplanets.
triceratops (Tool for Rating Interesting Candidate Exoplanets and Reliability Analysis of Transits Originating from Proximate Stars) validates planet candidates from the Transiting Exoplanet Survey Satellite (TESS). The code calculates the probabilities of a wide range of transit-producing scenarios using the primary transit of the planet candidate and preexisting knowledge of its host and nearby stars. It then uses the known properties of these stars to calculate star-specific priors for each scenario with estimates of stellar multiplicity and planet occurrence rates.
TESS-Point converts astronomical target coordinates given in right ascension and declination to detector pixel coordinates for the MIT-led NASA Transiting Exoplanet Survey Satellite (TESS) spacecraft. The program can also provide detector pixel coordinates for a star by TESS input catalog identifier number and common astronomical name. Tess-Point outputs the observing sector number, camera number, detector number, and pixel column and row.
TESSreduce builds on lightkurve (ascl:1812.013) to reduce TESS data while preserving transient signals. It takes a TPF as input (supplied or constructed with TESScut (https://mast.stsci.edu/tesscut/). The background subtraction accounts for the smooth background and detector straps. In addition to background subtraction, TESSreduce also aligns images, performs difference imaging, detects transient events, and by using PS1 data, can calibrate TESS counts to physical flux or AB magnitudes.
tvguide determines whether stars and galaxies are observable by TESS. It uses an object's right ascension and declination and estimates the pointing of TESS's cameras using predicted spacecraft ephemerides to determine whether and for how long the object is observable with TESS. tvguide returns a file with two columns, the first the minimum number of sectors the target is observable for and the second the maximum.
contaminante helps find the contaminant transiting source in NASA's Kepler, K2 or TESS data. When hunting for transiting planets, sometimes signals come from neighboring contaminants. This package helps users identify where the transiting signal comes from in their data. The code uses pixel level modeling of the TargetPixelFile data from NASA's astrophysics missions that are processed with the Kepler pipeline. The output of contaminante is a Python dictionary containing the source location and transit depth, and a contaminant location and depth. It can also output a figure showing where the main target is centered in all available TPFs, what the phase curve looks like for the main target, where the transiting source is centered in all available TPFs, if a transiting source is located outside the main target, or the transiting source phase curve, if a transiting source is located outside the main target.
vetting contains simple, stand-alone Python tools for vetting transiting signals in NASA's Kepler, K2, and TESS data. The code performs a centroid test to look for significant changes in the centroid of a star during a transit or eclipse. vetting requires an installation of Python 3.8 or higher.
PSFMachine creates models of instrument effective Point Spread Functions (ePSFs), also called Pixel Response Functions (PRFs). These models are then used to fit a scene in a stack of astronomical images. PSFMachine is able to quickly derive photometry from stacks of Kepler and TESS images and separate crowded sources.