Morvan, M;
Tsiaras, A;
Nikolaou, N;
Waldmann, IP;
(2021)
PyLightcurve-torch: a transit modeling package for deep learning applications in PyTorch.
Publications of the Astronomical Society of the Pacific
, 133
(1021)
, Article 034505. 10.1088/1538-3873/abe6e8.
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Abstract
We present a new open source python package, based on PyLightcurve and PyTorch Paszke et al., tailored for efficient computation and automatic differentiation of exoplanetary transits. The classes and functions implemented are fully vectorised, natively GPU-compatible and differentiable with respect to the stellar and planetary parameters. This makes PyLightcurve-torch suitable for traditional forward computation of transits, but also extends the range of possible applications with inference and optimization algorithms requiring access to the gradients of the physical model. This endeavour is aimed at fostering the use of deep learning in exoplanets research, motivated by an ever increasing amount of stellar light curves data and various incentives for the improvement of detection and characterization techniques.
Type: | Article |
---|---|
Title: | PyLightcurve-torch: a transit modeling package for deep learning applications in PyTorch |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1088/1538-3873/abe6e8 |
Publisher version: | http://dx.doi.org/10.1088/1538-3873/abe6e8 |
Language: | English |
Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
Keywords: | Exoplanets, Transits, Photometry, Neural networks |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
URI: | https://discovery.ucl.ac.uk/id/eprint/10125418 |



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