SearcharxivSearch

arXiv subjects

Suwei Wang

Publications and source records attributed to Suwei Wang.

2 recordsLinked to original sources

A Fast Concentric-disk Contour Integration Method For Microlensing Limb-darkening Effect

Incorporating limb darkening is a computationally demanding step in contour-integration-based microlensing modeling. Conventional concentric-ring integration is incompatible with high-order quadrature schemes, limiting efficiency. We develop a new concentric-disk method, which reformulates the limb-darkening integral and enables the application of high-order adaptive quadrature, significantly accelerating the computation. While mainly demonstrated using a linear limb-darkening profile, the method readily extends to more general limb-darkening profiles. As a source effect, it applies to lens systems of any complexity. The method achieves a convergence rate scaling faster than $N_{\rm{uni}}^{-4}$ with the number of uniform-source magnification evaluations $N_{\rm{uni}}$, a significant improvement over the concentric-ring $N_{\rm{uni}}^{-2}$ scaling. For a relative accuracy of $10^{-6}$, the concentric-disk approach typically requires only $30\%$ or less of the computational cost of traditional algorithms. This method has been implemented in the binary-lens contour integration code \texttt{Twinkle}, providing an efficient and precise tool for analyzing current and future high-precision microlensing observations.

astro-ph.EP

Twinkle: A GPU-based binary-lens microlensing code with contour integration method

With the rapidly increasing rate of microlensing planet detections, microlensing modeling software faces significant challenges in computation efficiency. Here, we develop the Twinkle code, an efficient and robust binary-lens modeling software suite optimized for heterogeneous computing devices, especially GPUs. Existing microlensing codes have the issue of catastrophic cancellation that undermines the numerical stability and precision, and Twinkle resolves them by refining the coefficients of the binary-lens equation. We also devise an improved method for robustly identifying ghost images, thereby enhancing computational reliability. We have advanced the state of the art by optimizing Twinkle specifically for heterogeneous computing devices by taking into account the unique task and cache memory dispatching patterns of GPUs, while the compatibility with the traditional computing architectures of CPUs is still maintained. Twinkle has demonstrated an acceleration of approximately 2 orders of magnitude (>~100 times) on contemporary GPUs. The enhancement in computational speed of Twinkle will translate to the delivery of accurate and highly efficient data analysis for ongoing and upcoming microlensing projects. Both GPU and CPU versions of Twinkle are open-source and publicly available.

astro-ph.IM