lenscape: Fast, High-resolution $\Delta {\chi }^2$ Maps of Microlensing Events

Abstract

Planetary microlensing analyses rely on $\Delta {\chi }^2$ maps across the binary-lens separation $s$ and mass ratio $q$ to locate degenerate solutions and bound parameter uncertainties. Because every grid point requires a full light-curve optimization with finite-source magnifications, constructing these maps is often the primary computational bottleneck in event modeling. Here I present lenscape, an open-source Python package that accelerates $\Delta {\chi }^2$ mapping and suppresses local-minimum artifacts through multi-stage neighbor propagation and adaptive contour refinement. Applied to the planetary event MOA-2022-BLG-091, lenscape reproduces the published $50\times 50$ map in 14 minutes and generates a $400\times 400$ map in under two hours on a desktop computer.

arXiv commentAccepted for publication in AAS journals; 1 figure; code available at https://github.com/hippke/lenscape