A Method To Characterize the Wide-Angle Point Spread Function of Astronomical Images

TL;DR

This paper introduces a Bayesian method to accurately model the wide-angle PSF in astronomical images, enabling better analysis of faint structures by characterizing scattered light out to 20-25 arcminutes.

Contribution

A novel self-consistent Bayesian approach for modeling the extended wing of the wide-angle PSF in astronomical images, demonstrated with the Dragonfly Telephoto Array data.

Findings

Dragonfly PSF has lower scattered light than other telescopes.

The PSF wings are sufficiently low for optical cleanliness to be crucial.

The method accurately models PSF out to 20-25 arcminutes.

Abstract

Uncertainty in the wide-angle Point Spread Function (PSF) at large angles (tens of arcseconds and beyond) is one of the dominant sources of error in a number of important quantities in observational astronomy. Examples include the stellar mass and shape of galactic halos and the maximum extent of starlight in the disks of nearby galaxies. However, modeling the wide-angle PSF has long been a challenge in astronomical imaging. In this paper, we present a self-consistent method to model the wide-angle PSF in images. Scattered light from multiple bright stars is fitted simultaneously with a background model to characterize the extended wing of the PSF using a Bayesian framework operating on pixel-by-pixel level. The method is demonstrated using our software elderflower and is applied to data from the Dragonfly Telephoto Array to model its PSF out to 20-25 arcminutes. We compare the wide-angle PSF of Dragonfly to that of a number of other telescopes, including the SDSS PSF, and show that on scales of arcminutes the scattered light in the Dragonfly PSF is markedly lower than that of other wide-field imaging telescopes. The energy in the wings of the Dragonfly point-spread function is sufficiently low that optical cleanliness plays an important role in defining the PSF. This component of the PSF can be modelled accurately, highlighting the power of our self-contained approach.