Microlensing of non-singular black holes at finite size: a ray tracing approach
Jens Boos, Hao Hu
TL;DR
The paper tackles microlensing by static, spherically symmetric non-singular black holes and horizonless compact objects. It combines a fourth-order parametrized post-Newtonian (PPN) lensing framework for point sources with a ray-tracing approach and simple radiative transfer for extended sources to generate time-dependent lightcurves as a moving lens crosses the line of sight, validating the methods by recovering point-source results in the appropriate limit. The study finds that finite-size sources tend to exhibit larger magnifications for non-singular models at finite size, contrasting with the point-source Schwarzschild prediction, and provides detailed near-zone and far-zone analyses for the Hayward, Minkowski core, and Simpson--Visser metrics. The results offer observably testable signatures for regular black holes and horizonless counterparts, informing future microlensing surveys and constraining regulator-length scenarios in quantum-gravity-inspired spacetimes.
Abstract
We study the gravitational microlensing of various static and spherically symmetric non-singular black holes (and horizonless, non-singular compact objects of similar size). For pointlike sources we extend the parametrized post-Newtonian lensing framework to fourth order, whereas for extended sources we develop a ray tracing approach via a simple radiative transfer model. Modelling non-relativistic proper motion of the lens in front of a background star we record the apparent brightness as a function of time, resulting in a photometric lightcurve. Taking the star radius to smaller values, our numerical results approach the theoretical predictions for point-like sources. Compared to the Schwarzschild metric in an otherwise unmodified lensing geometry, we find that non-singular black hole models (and their horizonless, non-singular counterparts) at finite size tend to feature larger magnifications in microlensing lightcurves, contrary to the point-source prediction.
