Dazzle: Oversampled Image Reconstruction and Difference-Imaging Photometry for the Nancy Grace Roman Space Telescope

TL;DR

Dazzle is a software suite that enables high-precision difference imaging and transient detection in crowded stellar fields for the Nancy Grace Roman Space Telescope, improving microlensing event detection and photometry.

Contribution

We introduce Dazzle, a novel algorithm and software for constructing difference images and detecting transients with high accuracy in crowded fields, tailored for the Roman Space Telescope.

Findings

High recovery rates in synthetic tests for microlensing detection

Effective correction for dither offset errors in difference imaging

Open-source Python implementation accessible to the community

Abstract

We present algorithms and software for constructing high-precision difference images to detect and measure transients, such as microlensing events, in crowded stellar fields using the Nancy Grace Roman Space Telescope. Our method generates difference images by subtracting an over-sampled reference, with iterative masking to address outlier pixels. We also provide an analytic correction for small dither offset errors. Microlensing event detection is achieved through a three-dimensional matched-filtering technique, optimized with Gaussian kernels to capture varying event durations, and verified through synthetic tests with high recovery rates. Transient photometry is performed via PSF fitting on difference images, using Nelder-Mead optimization for sub-pixel accuracy. The software, Dazzle, is available as an open-source Python package built on widely used libraries, offering accessible tools for the detection and characterization of transient phenomena in crowded fields.