How to avoid X'es around point sources in maximum likelihood CMB maps

Abstract

The maximum likelihood estimator for CMB map-making is optimal and unbiased as long as the data model is correct, but in practice it rarely is, with model errors including sub-pixel structure and instrumental problems like time-variable gain and pointing errors. In the presence of such errors, the solution is biased, with the local error in each pixel leaking outwards along the scanning pattern by a noise correlation length. The most important sources of such leakage are strong point sources, and for common scanning patterns the leakage manifests as an X around each such source. I discuss why this happens, and present several old and new methods for mitigating and/or eliminating this leakage, along with a small stand-alone TOD simulator and map-maker in Python that implements them.

16 pages, 4 figures, accepted for publication in JCAP

References in corpus (1)

• Planck 2018 results. III. High Frequency Instrument data processing and frequency maps

Cited by in corpus (10)

• The Atacama Cosmology Telescope: DR4 Maps and Cosmological Parameters
• The Atacama Cosmology Telescope: Component-separated maps of CMB temperature and the thermal Sunyaev-Zel'dovich effect
• The Atacama Cosmology Telescope: Microwave Intensity and Polarization Maps of the Galactic Center
• The Atacama Cosmology Telescope: Measurement and Analysis of 1D Beams for DR4
• Inpainting Galactic Foreground Intensity and Polarization maps using Convolutional Neural Network
• Large-scale power loss in ground-based CMB mapmaking
• The Simons Observatory: Beam characterization for the Small Aperture Telescopes
• Filtering in CMB data analysis with application to ACT DR4 and Planck observations
• The Atacama Cosmology Telescope: Machine Learning Driven Tools for Detecting Millimeter Sources in Timestream Pre-processing
• Mitigating point-source contamination in CMB polarization: a Generalized Point Spread Function fitting approach