Removing Atmospheric Fringes from Zwicky Transient Facility i-Band Images using Principal Component Analysis

TL;DR

This paper introduces a PCA-based method to remove atmospheric fringes from Zwicky Transient Facility i-band images, improving photometric accuracy for faint sources and multi-epoch data.

Contribution

The authors develop and publicly release a PCA-based algorithm for effectively modeling and removing atmospheric fringes from astronomical images.

Findings

Significant reduction in background noise after fringe removal

Improved photometric precision for faint sources

Enhanced accuracy in multi-epoch image co-additions

Abstract

The Zwicky Transient Facility is a time-domain optical survey that has substantially increased our ability to observe and construct massive catalogs of astronomical objects by use of its 47 square degree camera that can observe in multiple filters. However the telescope's i-band filter suffers from significant atmospheric fringes that reduce photometric precision, especially for faint sources and in multi-epoch co-additions. Here we present a method for constructing models of these atmospheric fringes using Principal Component Analysis that can be used to identify and remove these artifacts from contaminated images. In addition, we present the Uniform Background Indicator as a quantitative measurement of the reduced correlated background noise and photometric error present after removing fringes. We conclude by evaluating the effect of our method on measuring faint sources through the injection and recovery of artificial stars in both single-image epochs and co-additions. Our method for constructing atmospheric fringe models and applying those models to produce cleaned images is available for public download in the open source python package \href{https://github.com/MichaelMedford/fringez}{fringez}.