Robust statistics towards detection of the 21 cm signal from the Epoch of Reionization. Issue 4 (8th May 2019)

• Record Type: Journal Article
• Authors: Trott, Cathryn M; Fu, Shih Ching; Murray, S G; Jordan, C H; Line, J L B; Barry, N; Byrne, R; Hazelton, B J; Hasegawa, K; Joseph, R; Kaneuji, T; Kubota, K; Li, W; Lynch, C; McKinley, B; Mitchell, D A; Morales, M F; Pindor, B; Pober, J C; Rahimi, M; Takahashi, K; Tingay, S J; Wayth, R B; Webster, R L; Wilensky, M; Wyithe, J S B; Yoshiura, S; Zheng, Q; Walker, M
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 486:Issue 4(2019)
• Page Start: 5766
• Page End: 5784
• Publication Date: 2019-05-08
• Subjects: instrumentation: interferometers -- methods: statistical; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stz1207
• Languages: English
• ISSNs: 0035-8711

Abstract

Abstract: We explore methods for robust estimation of the 21 cm signal from the Epoch of Reionization (EoR). A Kernel Density Estimator (KDE) is introduced for measuring the spatial temperature fluctuation power spectrum from the EoR. The KDE estimates the underlying probability distribution function of fluctuations as a function of spatial scale, and contains different systematic biases and errors to the typical approach to estimating the fluctuation power spectrum. Extraction of histograms of visibilities allows moments analysis to be used to discriminate foregrounds from 21 cm signal and thermal noise. We use the information available in the histograms, along with the statistical dis-similarity of foregrounds from two independent observing fields, to robustly separate foregrounds from cosmological signal, while making no assumptions about the Gaussianity of the signal. Using two independent observing fields to robustly discriminate signal from foregrounds is crucial for the analysis presented in this paper. We apply the techniques to 13 h of Murchison Widefield Array EoR data over two observing fields. We compare the output to that obtained with a comparative power spectrum estimation method, and demonstrate the reduced foreground contamination using this approach. Using the second moment obtained directly from the KDE distribution functions yields a factor of 2–3 improvement in power for k < 0.3 h Mpc −1 compared with a matched delay space power estimator, while weighting data by additional statistics does not offer significant improvement beyond that available for thermal noise-only weights.