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HI Intensity Mapping with the MIGHTEE Survey: First Results of the HI Power Spectrum

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arxiv 2501.17564 v2 pith:UJOJKRFM submitted 2025-01-29 astro-ph.CO

HI Intensity Mapping with the MIGHTEE Survey: First Results of the HI Power Spectrum

classification astro-ph.CO
keywords mighteepowerspectrumdatalesssimsurveyfirstintensity
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present the first results of the HI intensity mapping power spectrum analysis with the MeerKAT International GigaHertz Tiered Extragalactic Exploration (MIGHTEE) survey. We use data covering $\sim$4 square degrees in the COSMOS field using a frequency range 962.5 MHz to 1008.42 MHz, equivalent to HI emission in $0.4<z<0.48$. The data consists of 15 pointings with a total of 94.2 hours on-source. We verify the suitability of the MIGHTEE data for HI intensity mapping by testing for residual systematics across frequency, baselines and pointings. We also vary the window used for HI signal measurements and find no significant improvement using stringent Fourier mode cuts. Averaging in the power spectrum domain, i.e. using incoherent averaging, we calculate the first upper limits from MIGHTEE on the HI power spectrum at scales 0.5 Mpc$^{-1} \lesssim k \lesssim$ 10 Mpc$^{-1}$. We obtain the best 1$\sigma$ upper limit of 28.6 mK$^{2}$Mpc${^3}$ on $k\sim$2 Mpc$^{-1}$. Our results are consistent with the power spectrum detected with observations in the DEEP2 field with MeerKAT. The data we use here constitutes a small fraction of the MIGHTEE survey and demonstrates that combined analysis of the full MIGHTEE survey can potentially detect the HI power spectrum at $z\lesssim0.5$ in the range 0.1 Mpc$^{-1} \lesssim k \lesssim$ 10 Mpc$^{-1}$ or quasi-linear scales.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Seeing Wiggles without Seeing Wiggles: BAO Recovery in 21 cm Intensity Mapping with Deep Learning

    astro-ph.CO 2026-02 conditional novelty 6.0

    A 3D U-Net trained only on BAO-free 21 cm simulations recovers BAO wiggles from small-scale modes outside the foreground wedge, indicating physical mode coupling.

  2. Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation

    astro-ph.CO 2025-11 conditional novelty 5.0

    A two-channel UNet combining frequency differencing and PCA preprocessing recovers the 21-cm HI power spectrum at large scales under realistic beam effects, improving cross-correlation by 5-8% over single-channel baselines.

  3. Cosmology with HI Intensity Mapping

    astro-ph.CO 2026-07 accept novelty 4.0

    SKAO HI intensity mapping forecasts yield competitive LambdaCDM constraints (e.g. H0 to ~0.3 km/s/Mpc optimistic) via power spectrum, BAO, bispectrum and stacking, complementary to CMB and optical surveys.