Subband Image Reconstruction using Differential Chromatic Refraction

TL;DR

This paper presents a new method to use atmospheric refraction effects in astronomical images to infer the spectral energy distribution of sources, enabling higher spectral resolution imaging from survey data.

Contribution

It introduces a novel deconvolution technique that leverages differential chromatic refraction to recover spectral information and improve image resolution.

Findings

Successfully infers spectral energy distributions from DCR signals.

Produces higher spectral resolution images than original data.

Applicable to large-scale surveys like LSST.

Abstract

Refraction by the atmosphere causes the positions of sources to depend on the airmass through which an observation was taken. This shift is dependent on the underlying spectral energy of the source and the filter or bandpass through which it is observed. Wavelength-dependent refraction within a single passband is often referred to as differential chromatic refraction (DCR). With a new generation of astronomical surveys undertaking repeated observations of the same part of the sky over a range of different airmasses and parallactic angles, DCR should be a detectable and measurable astrometric signal. In this paper we introduce a novel procedure that takes this astrometric signal and uses it to infer the underlying spectral energy distribution of a source; we solve for multiple latent images at specific wavelengths via a generalized deconvolution procedure built on robust statistics. We demonstrate the utility of such an approach for estimating a partially deconvolved image, at higher spectral resolution than the input images, for surveys such as the Large Synoptic Survey Telescope (LSST).