PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch

TL;DR

PyLightcurve-torch is an open-source Python package combining transit modeling with deep learning capabilities in PyTorch, enabling efficient, GPU-compatible, and differentiable computations for exoplanet research.

Contribution

It introduces a fully vectorized, GPU-compatible, and differentiable transit modeling package that integrates with PyTorch, facilitating deep learning applications in exoplanet studies.

Findings

Efficient computation of exoplanet transits with GPU acceleration.

Differentiable models enabling gradient-based inference and optimization.

Supports large-scale light curve analysis for exoplanet detection.

Abstract

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.