lightcurver: A Python Pipeline for Precise Photometry of Multiple-Epoch Wide-Field Images

TL;DR

lightcurver is a Python pipeline that extracts precise, multi-epoch light curves from crowded field astronomical images, leveraging advanced PSF modeling and relative zeropoint calibration for efficient, high-quality time series analysis.

Contribution

It introduces a semi-automatic, maintainable pipeline that combines PSF modeling and relative zeropoint calibration to improve photometry of blended targets in crowded fields.

Findings

Produces high-precision light curves for blended sources

Enables daily analysis suitable for large surveys

Uses STARRED for state-of-the-art PSF modeling

Abstract

lightcurver is a photometric pipeline for time series astronomical imaging data, designed for the semi-automatic extraction of precise light curves from small, blended targets. Such targets include, but are not limited to, lensed quasars, supernovae, or Cepheids in crowded fields. lightcurver leverages STARRED (Michalewicz et al., 2023; Millon et al., 2024) to generate state-of-the-art empirical point spread function (PSF) models for each image. It then determines the relative zeropoints between epochs by combining the PSF-photometry fluxes of several stars in the field of view. Subsequently, STARRED is used again to simultaneously model the calibrated pixels of the region of interest across all epochs. This process yields light curves of the point sources and a high-resolution image model of the region of interest, cumulating the signal from all epochs. lightcurver aims to be maintainable, fast, and incremental in its processing approach. As such, it can enable the daily photometric analysis of a large number of blended targets in the context of the upcoming Rubin Observatory Legacy Survey of Space and Time.