ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

Searching for codes credited to 'Santos, N. C.'

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[ascl:1504.021] SOAP 2.0: Spot Oscillation And Planet 2.0
Dumusque, Xavier; Boisse, I.; Santos, N. C.

SOAP (Spot Oscillation And Planet) 2.0 simulates the effects of dark spots and bright plages on the surface of a rotating star, computing their expected radial velocity and photometric signatures. It includes the convective blueshift and its inhibition in active regions.

[ascl:1504.020] BGLS: A Bayesian formalism for the generalised Lomb-Scargle periodogram
Mortier, A.; Faria, J. P.; Correia, C. M.; Santerne, A.; Santos, N. C.

BGLS calculates the Bayesian Generalized Lomb-Scargle periodogram. It takes as input arrays with a time series, a dataset and errors on those data, and returns arrays with sampled periods and the periodogram values at those periods.

[ascl:2505.009] BEM: Random forest for exoplanets
Ulmer-Moll, S.; Santos, N. C.; Figueira, P.; Brinchmann, J.; Faria, J. P.

BEM predicts the radius of exoplanets based on their planetary and stellar parameters. The code uses the random forests machine learning algorithm to derive reliable radii, especially for planets between 4 R⊕ and 20 R⊕ for which the error is under 25%. BEM computes error bars for the radius predictions and can also create diagnostic plots.

[ascl:2002.005] ODUSSEAS: Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes
Antoniadis-Karnavas, A.; Sousa, S. G.; Delgado-Mena, E.; Santos, N. C.; Teixeira, G. D. C.; Neves, V.

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:2302.014] kima: Exoplanet detection in RVs with DNest4 and GPs
Faria, J. P.; Santos, N. C.; Figueira, P; Brewer, B. J.

kima fits Keplerian curves to a set of RV measurements, using the Diffusive Nested Sampling (ascl:1010.029) algorithm to sample the posterior distribution for the model parameters. Additionally, the code can calculate the fully marginalized likelihood of a model with a given number of Keplerians and also infer the number of Keplerian signals detected in a given dataset. kima implements dedicated models for different analyses of a given dataset. The models share a common organization, but each has its own parameters (and thus priors) and settings.

[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: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).