论文标题
Pylightcurve-torch：用于Pytorch深度学习应用的公交建模包
PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch
论文作者
论文摘要
我们提出了一个基于Pylightcurve和Pytorch的新开源Python软件包，该软件包量身定制，用于有效的计算和自动差异化外部电视转物。相对于恒星和行星参数，实现的类和功能是完全矢量化的，本地与GPU的兼容和可区分的。这使得PylightCurve-Torch适用于传统的转运远期计算，但还通过推理和优化算法扩展了可能的应用程序范围，需要访问物理模型的梯度。这项努力旨在促进在系外行星研究中使用深度学习，这是出于不断增加的恒星光曲线数据和各种激励措施来改善检测和表征技术。
We present a new open source python package, based on PyLightcurve and PyTorch, 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 optimisation 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 characterisation techniques.
