KMT-2025-BLG-1314 and KMT-2025-BLG-1392: two microlensing planetary/brown-dwarf candidates analyzed with differentiable code

TL;DR

This paper demonstrates the use of a differentiable microlensing modeling package, microlux, to analyze complex binary-lens microlensing events, effectively resolving degeneracies and improving parameter inference with Hamiltonian Monte Carlo.

Contribution

The paper introduces microlux, a JAX-based differentiable modeling tool, and applies it to analyze two microlensing events, showcasing its advantages over traditional methods in handling complex posterior distributions.

Findings

microlux effectively models complex microlensing events

HMC outperforms MCMC in bimodal posterior inference

Degeneracies in microlensing solutions are characterized

Abstract

Analysis of binary-lens microlensing events typically requires intensive computation because of the multimodal and complex posterior distributions. With the recent development of the JAX-based differentiable binary-lensing modeling package microlux, we present an analysis of two microlensing events with planet/brown-dwarf candidates, KMT-2025-BLG-1314 and KMT-2025-BLG-1392. Both events exhibit the "Close/Wide" degeneracy, and KMT-2025-BLG-1314 suffers from the "Planet/Binary" degeneracy and a recently recognized "Point/Finite" degeneracy among the planetary solutions. For KMT-2025-BLG-1314, the binary mass ratio is $\log q \sim -3.5$ for the planetary solutions and $\log q > -1.5$ for the binary solutions, while for KMT-2025-BLG-1392, we find $\log q \sim -1.3$. We show that for the analysis of KMT-2025-BLG-1314, Hamiltonian Monte Carlo (HMC), enabled by microlux, provides robust parameter inference and outperforms traditional Markov chain Monte Carlo (MCMC) methods in the presence of bimodal posteriors.