MeerKLASS simulations: Mitigating 1/ 𝑓 noise for auto-correlation intensity mapping measurements

Melis O. Irfan, Yichao Li, Mario G. Santos, Philip Bull, Junhua Gu, Steven Cunnington, Keith Grainge, Jingying Wang

Research output: Contribution to journal › Article › peer-review

Abstract

We present and compare several methods to mitigate time-correlated (1/ 𝑓 ) noise within the Hi intensity mapping component of the MeerKAT Large Area Synoptic Survey (MeerKLASS). By simulating scan strategies, the Hi signal, white and correlated noise, we assess the ability of various data processing pipelines to recover the power spectrum of Hi brightness temperature fluctuations. We use MeerKAT pilot data to assess the level of 1/ 𝑓 noise expected for the MeerKLASS survey and use these measurements to create realistic levels of time-correlated
noise for our simulations. We find the time-correlated noise component within the pilot data to be between 16 and 23 times higher than the white noise level at the scale of 𝑘 = 0.04 Mpc−1. Having determined that the MeerKAT 1/ 𝑓 noise is partially correlated across all the frequencychannels, we employ Singular Value Decomposition (SVD) as a technique to remove 1/ 𝑓 noise but find that over-cleaning results in the removal of Hi power at large (angular and radial) scales; a power loss of 20 per cent is seen for a 2-mode SVD clean at the scale of
𝑘 = 0.04 Mpc−1.  We compare the impact of map-making using weighting by the full noise covariance (i.e. including a 1/ 𝑓 component), as opposed to just a simple unweighted binning, finding that including the time-correlated noise information reduces the excess power added by 1/ 𝑓 noise by up to 30 per cent.
Original languageEnglish
JournalMonthly Notices of the Royal Astronomical Society
Publication statusAccepted/In press - 3 Nov 2023

Keywords

  • cosmology
  • large-scale structure of Universe
  • methods
  • statistical
  • data analysis

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