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.

我们提出了一个新的开源 python 包，它基于 PyLightcurve 和 PyTorch Paszke 等人，专为有效计算和自动区分系外行星凌日而量身定制。实现的类和函数是完全矢量化的，与原生 GPU 兼容，并且在恒星和行星参数方面可微分。这使得 PyLightcurve-torch 适用于传输的传统前向计算，但也通过需要访问物理模型梯度的推理和优化算法扩展了可能的应用范围。这项努力旨在促进深度学习在系外行星研究中的应用，其动力来自于不断增加的恒星光变曲线数据以及各种改进检测和表征技术的激励措施。