2D-1D Wavelet Reconstruction As A Tool For Source Finding In Spectroscopic Imaging Surveys

TL;DR

This paper adapts wavelet-based denoising techniques to 2D-1D spectroscopic data cubes, enhancing source detection in imaging surveys by implementing and evaluating a specialized wavelet decomposition method.

Contribution

It introduces a 2D-1D wavelet decomposition approach tailored for spectroscopic data, improving denoising efficiency for source finding in 3D datasets.

Findings

The method effectively denoises spectroscopic data cubes.

Simulations show improved source detection accuracy.

The approach integrates well into existing pipelines.

Abstract

Today, image denoising by thresholding of wavelet coefficients is a commonly used tool for 2D image enhancement. Since the data product of spectroscopic imaging surveys has two spatial and one spectral dimension, the techniques for denoising have to be adapted to this change in dimensionality. In this paper we will review the basic method of denoising data by thresholding wavelet coefficients and implement a 2D-1D wavelet decomposition to obtain an efficient way of denoising spectroscopic data cubes. We conduct different simulations to evaluate the usefulness of the algorithm as part of a source finding pipeline.