2015
DOI: 10.1093/mnras/stv894
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pytransit: fast and easy exoplanet transit modelling in python

Abstract: 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 se… Show more

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Cited by 183 publications
(112 citation statements)
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“…We model CLV and RM contributions by simulating the transmission lightcurve of a planet without an atmosphere at the same phases as the data (Section 5.2). We then divide the observed lightcurve by the model, and fit the residuals using the PyTransit package (Parviainen 2015). We estimate limb-darkening coefficients with the LDTk package (Parviainen & Aigrain 2015).…”
Section: Lightcurve Analysismentioning
confidence: 99%
“…We model CLV and RM contributions by simulating the transmission lightcurve of a planet without an atmosphere at the same phases as the data (Section 5.2). We then divide the observed lightcurve by the model, and fit the residuals using the PyTransit package (Parviainen 2015). We estimate limb-darkening coefficients with the LDTk package (Parviainen & Aigrain 2015).…”
Section: Lightcurve Analysismentioning
confidence: 99%
“…The analyses were carried out with a custom Python code based on PyTransit v2 5 (Parviainen 2015;Parviainen et al 2019), which includes a physics-based contamination model based on the PHOENIX-calculated stellar spectrum library by Husser et al (2013). The limb darkening computations were carried out with LDTk 6 (Parviainen & Aigrain 2015), and MCMC sampling was carried out with emcee All the code for the analyses presented in this paper is available from GitHub as Python code and Jupyter notebooks from https://github.com/hpparvi/parviainen_2019b_ toi_263.…”
Section: Overviewmentioning
confidence: 99%
“…All observations covered from 2 to 3.2 hours around the expected midtransit time, and were carried simultaneously in the r , i , and z passbands with a common exposure time of 10 seconds. The photometry was done with the MuSCAT2 pipeline based on PyTransit (Parviainen 2015) and LDTk (Parviainen & Aigrain 2015).…”
Section: Photometrymentioning
confidence: 99%