Multiple-component Decomposition from Millimeter Single-channel Data
Abstract
We present an implementation of a blind source separation algorithm to remove foregrounds off millimeter surveys made by single-channel instruments. In order to make possible such a decomposition over single-wavelength data, we generate levels of artificial redundancy, then perform a blind decomposition, calibrate the resulting maps, and lastly measure physical information. We simulate the reduction pipeline using mock data: atmospheric fluctuations, extended astrophysical foregrounds, and point-like sources, but we apply the same methodology to the Aztronomical Thermal Emission Camera/ASTE survey of the Great Observatories Origins Deep Survey-South (GOODS-S). In both applications, our technique robustly decomposes redundant maps into their underlying components, reducing flux bias, improving signal-to-noise ratio, and minimizing information loss. In particular, GOODS-S is decomposed into four independent physical components: one of them is the already-known map of point sources, two are atmospheric and systematic foregrounds, and the fourth component is an extended emission that can be interpreted as the confusion background of faint sources.
- Publication:
-
The Astrophysical Journal Supplement Series
- Pub Date:
- March 2018
- DOI:
- arXiv:
- arXiv:1711.08456
- Bibcode:
- 2018ApJS..235...12R
- Keywords:
-
- atmospheric effects;
- methods: statistical;
- submillimeter: diffuse background;
- submillimeter: galaxies;
- techniques: image processing;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Astrophysics - Astrophysics of Galaxies;
- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Accepted in ApJS