An Efficient and Optimal Filter for Identifying Point Sources in Millimeter/Sub-Millimeter Wavelength Sky Maps

TL;DR

This paper introduces a computationally efficient optimal filter for detecting point sources in millimeter/sub-millimeter sky maps, accounting for noise frequency dependence and coverage non-uniformities, outperforming standard matched filters.

Contribution

The paper presents a novel optimal filter that improves point source detection by incorporating noise and coverage variations, with practical implementation details for AzTEC data.

Findings

The new filter enhances detection reliability over standard methods.

FFT-based computations make the method computationally feasible.

Application to AzTEC maps demonstrates improved source identification.

Abstract

A new technique for reliably identifying point sources in millimeter/sub-millimeter wavelength maps is presented. This method accounts for the frequency dependence of noise in the Fourier domain as well as non-uniformities in the coverage of a field. This optimal filter is an improvement over commonly-used matched filters that ignore coverage gradients. Treating noise variations in the Fourier domain as well as map space is traditionally viewed as a computationally intensive problem. We show that the penalty incurred in terms of computing time is quite small due to casting many of the calculations in terms of FFTs and exploiting the absence of sharp features in the noise spectra of observations. Practical aspects of implementing the optimal filter are presented in the context of data from the AzTEC bolometer camera. The advantages of using the new filter over the standard matched filter are also addressed in terms of a typical AzTEC map.