A Unified Analytic Framework for Microlensing Caustics: Geode Solutions and Hyper--Catalan Signatures

TL;DR

This paper introduces a unified analytic framework for microlensing caustics using hyper-Catalan recurrences, enabling precise image modeling near caustics with certified series expansions and new signatures for analysis.

Contribution

It develops a preparation-invariant analytic method employing hyper-Catalan recurrences for microlensing caustic analysis, covering complex fold and cusp structures with certified, high-precision series solutions.

Findings

Achieves machine-precision image recovery within certified domains.

Defines HC signature and spectrum for quantifying sparsity and stability.

Demonstrates method on complex lens geometries with accurate results.

Abstract

We give a preparation-invariant analytic description of image formation near microlensing caustics. After a local Weierstrass preparation at any multiple image (order $d\ge2$), the lens mapping reduces to a single geode variable $m$ satisfying $m=U\,\varphi(m)$, where $U$ is a prepared source coordinate and $\varphi$ is an image-side kernel. The coefficients of $m(U)$ obey closed Hyper-Catalan (HC) recurrences, allowing termwise derivatives and truncation control from the characteristic system. We also use the same form for a short HC predictor-corrector: evaluate the series within its certified radius and apply a brief Newton polish near the boundary. We define an HC signature (first nonzero kernel coefficients) and an HC spectrum (branch points and analyticity radius $\rho_U$), which quantify sparsity, stiffness, and safe evaluation domains. The construction covers folds and cusps of any global degree. On a binary fold and cusp, an artificial decic with a resonant unit, and two triple-lens cusps of a challenging geometry, HC seeds plus a few Newton steps recover the exact images to machine precision within the certified domain and maintain continuity under continuation. The resulting single-series templates (with $(\mathrm{Sig}_R,\rho_U)$ metadata) are ready for photometric and astrometric modeling.