[ascl:2506.004]
TESS-cont: TESS contamination tool
Castro-González, A.;
Lillo-Box, J.;
Armstrong, D. J.;
Acuña, L.;
Aguichine, A.;
Bourrier, V.;
Gandhi, S.;
Sousa, S. G.;
Delgado-Mena, E.;
Moya, A.;
Adibekyan, V.;
Correia, A. C. M.;
Barrado, D.;
Damasso, M.;
Winn, J. N.;
Santos, N. C.;
Barkaoui, K.;
Barros, S. C. C.;
Benkhaldoun, Z.;
Bouchy, F.;
Briceño, C.;
Caldwell, D. A.;
Collins, K. A.;
Essack, Z.;
Ghachoui, M.;
Gillon, M.;
Hounsell, R.;
Jehin, E.;
Jenkins, J. M.;
Keniger, M. A. F.;
Law, N.;
Mann, A. W.;
Nielsen, L. D.;
Pozuelos, F. J.;
Schanche, N.;
Seager, S.;
Tan, T. -G.;
Timmermans, M.;
Villaseñor, J.;
Watkins, C. N.;
Ziegler, C.
TESS-cont quantifies the flux fraction coming from nearby stars in the TESS photometric aperture of any observed target. The package identifies the main contaminant Gaia DR2/DR3 sources, quantifies their individual and total flux contributions to the aperture, and determines whether any of these stars could be the origin of the observed transit and variability signals. Written in Python, TESS-cont is based on building the pixel response functions (PRFs) of nearby Gaia sources and computing their flux distributions across the TESS Target Pixel Files (TPFs) or Full Frame Images (FFIs).
[ascl:2412.027]
mr-plotter: Mass-radius diagrams plotter
Castro-González, A.;
Demangeon, O. D. S.;
Lillo-Box, J.;
Lovis, C.;
Lavie, B.;
Adibekyan, V.;
Acuña, L.;
Deleuil, M.;
Aguichine, A.;
Zapatero Osorio, M. R.;
Tabernero, H. M.;
Davoult, J.;
Alibert, Y.;
Santos, N.;
Sousa, S. G.;
Antoniadis-Karnavas, A.;
Borsa, F.;
Winn, J. N.;
Allende Prieto, C.;
Figueira, P.;
Jenkins, J. M.;
Sozzetti, A.;
Damasso, M.;
Silva, A. M.;
Astudillo-Defru, N.;
Barros, S. C. C.;
Bonfils, X.;
Cristiani, S.;
Di Marcantonio, P.;
González Hernández, J. I.;
Curto, G. Lo;
Martins, C. J. A. P.;
Nunes, N. J.;
Palle, E.;
Pepe, F.;
Seager, S.;
Suárez Mascareño, A.
Mister plotter (mr-plotter) creates paper-quality mass-radius diagrams based on a wide range of state-of-the-art models of planetary interiors and atmospheres. It can be used to contextualize planets and infer their possible internal structures. It can also be used to search for correlations at a population level with its color-coding option based on any property collected in the
NASA Exoplanet Archive,
PlanetS, and
Exoplanet.eu catalogs. mr-plotter can also produce article-ready two-column plots.
[ascl:2312.034]
pycheops: Light curve analysis for ESA CHEOPS data
Maxted, P. F. L.;
Ehrenreich, D.;
Wilson, T. G.;
Alibert, Y.;
Cameron, A. Collier;
Hoyer, S.;
Sousa, S. G.;
Olofsson, G.;
Bekkelien, A.;
Deline, A.;
Delrez, L.;
Bonfanti, A.;
Borsato, L.;
Alonso, R.;
Anglada Escudé, G.;
Barrado, D.;
Barros, S. C. C.;
Baumjohann, W.;
Beck, M.;
Beck, T.;
Benz, W.;
Billot, N.;
Biondi, F.;
Bonfils, X.;
Brandeker, A.;
Broeg, C.;
Bárczy, T.;
Cabrera, J.;
Charnoz, S.;
Corral Van Damme, C.;
Csizmadia, Sz;
Davies, M. B.;
Deleuil, M.;
Demangeon, O. D. S.;
Demory, B. -O.;
Erikson, A.;
Florén, H. G.;
Fortier, A.;
Fossati, L.;
Fridlund, M.;
Futyan, D.;
Gandolfi, D.;
Gillon, M.;
Guedel, M.;
Guterman, P.;
Heng, K.;
Isaak, K. G.;
Kiss, L.;
Laskar, J.;
Lecavelier des Etangs, A.;
Lendl, M.;
Lovis, C.;
Magrin, D.;
Nascimbeni, V.;
Ottensamer, R.;
Pagano, I.;
Pallé, E.;
Peter, G.;
Piotto, G.;
Pollacco, D.;
Pozuelos, F. J.;
Queloz, D.;
Ragazzoni, R.;
Rando, N.;
Rauer, H.;
Reimers, C.;
Ribas, I.;
Salmon, S.;
Santos, N. C.;
Scandariato, G.;
Simon, A. E.;
Smith, A. M. S.;
Steller, M.;
Swayne, M. I.;
Szabó, Gy M.;
Ségransan, D.;
Thomas, N.;
Udry, S.;
Van Grootel, V.;
Walton, N. A.
pycheops analyzes CHEOPS light curve data. The models in the package can also be applied to other types of data. pycheops includes a "cook book" and examples; in addition, it provides a command-line tool that aids in the preparation of observing requests for CHEOPS observers.
[ascl:2002.005]
ODUSSEAS: Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes
ODUSSEAS (Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes) uses machine learning to derive the Teff and [Fe/H] of M dwarf stars by using their optical spectra obtained by different spectrographs with different resolutions. The software uses the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and the machine learning Python package scikit-learn (https://scikit-learn.org/stable/) to predict the stellar parameters.
[ascl:2006.015]
ARCHI: Add-on pipeline module for background star analysis from CHEOPS data
The CHaracterizing ExOPlanet Satellite (CHEOPS) mission pipeline provides photometry for the central star in its field; ARCHI takes in data from the CHEOPS mission pipeline, analyzes the background stars, and determines the photometry of these stars, thus creating the possibility of producing photometric time-series of several close-by targets at once, in addition to using different stars in the image to calibrate systematic errors.
[ascl:1209.007]
TMCalc: Fast estimation of stellar metallicity [Fe/H]
TMCalc is a C code developed as an extension to ARES. Using the line list given, the code can be used as a precise and fast indicator of the spectroscopic temperature and metallicity for dwarf FKG stars with effective temperatures ranging from 4500 K to 6500 K and with [Fe/H] ranging from -0.8 dex to 0.4 dex.
[ascl:1205.009]
ARES: Automatic Routine for line Equivalent widths in stellar Spectra
ARES was developed for the measurement of Equivalent Width of absortion lines in stellar spectra; it can also be used to determine fundamental spectroscopic stellar parameters.The code reads a 1D FITS spectra and fits the requested lines in order to calculate the Equivalent width. The code is written in C++ based on the standard method of determining EWs. It automates the manual procedure that one normally carries out when using interactive routines such as the splot routine implemented in IRAF.