Bayes-SCF: A Bayesian filter to mitigate foreground leakage in the 21-cm power spectrum
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911546462961664 |
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| author | Elahi, Khandakar Md Asif |
| author_facet | Elahi, Khandakar Md Asif |
| contents | Missing channels in radio-interferometric visibility data can introduce systematic artifacts into the estimated 21-cm power spectrum. A common workaround is to first estimate the two-frequency correlation $C(Δν)$ and then Fourier-transform it to obtain the power spectrum $P(k_\parallel)$. This procedure yields an unbiased estimate when the signal is statistically homogeneous (ergodic) along the line-of-sight, but it fails in the presence of non-ergodic foregrounds. Smooth Component Filtering (SCF) has recently been proposed as a solution to this problem, in which the dominant non-ergodic (spectrally smooth) component is removed prior to estimating $C(Δν)$. In existing implementations, the smooth component is estimated by convolving the visibilities with a Hann window along the frequency axis. We demonstrate that this Hann-based SCF performs adequately only when foregrounds are extremely spectrally smooth, i.e., when they possess a long frequency-correlation length. In contrast, it breaks down when foregrounds exhibit short correlation lengths, as is frequently encountered in real observations. We introduce a Bayesian extension, Bayes-SCF, based on Gaussian Process (GP) regression, which overcomes this limitation. Bayes-SCF models the smooth component via a covariance function with a fixed correlation length, enabling a controlled and data-driven filtering. Using simulated data, we show that Bayes-SCF robustly recovers the input model 21-cm power spectrum even in the presence of spectrally unsmooth foregrounds. Bayes-SCF is also effective in a delay-spectrum approach. The primary trade-off introduced by the Bayesian framework is increased computational cost; future work will focus on optimizing the algorithm and applying it to real MWA data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25543 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bayes-SCF: A Bayesian filter to mitigate foreground leakage in the 21-cm power spectrum Elahi, Khandakar Md Asif Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Missing channels in radio-interferometric visibility data can introduce systematic artifacts into the estimated 21-cm power spectrum. A common workaround is to first estimate the two-frequency correlation $C(Δν)$ and then Fourier-transform it to obtain the power spectrum $P(k_\parallel)$. This procedure yields an unbiased estimate when the signal is statistically homogeneous (ergodic) along the line-of-sight, but it fails in the presence of non-ergodic foregrounds. Smooth Component Filtering (SCF) has recently been proposed as a solution to this problem, in which the dominant non-ergodic (spectrally smooth) component is removed prior to estimating $C(Δν)$. In existing implementations, the smooth component is estimated by convolving the visibilities with a Hann window along the frequency axis. We demonstrate that this Hann-based SCF performs adequately only when foregrounds are extremely spectrally smooth, i.e., when they possess a long frequency-correlation length. In contrast, it breaks down when foregrounds exhibit short correlation lengths, as is frequently encountered in real observations. We introduce a Bayesian extension, Bayes-SCF, based on Gaussian Process (GP) regression, which overcomes this limitation. Bayes-SCF models the smooth component via a covariance function with a fixed correlation length, enabling a controlled and data-driven filtering. Using simulated data, we show that Bayes-SCF robustly recovers the input model 21-cm power spectrum even in the presence of spectrally unsmooth foregrounds. Bayes-SCF is also effective in a delay-spectrum approach. The primary trade-off introduced by the Bayesian framework is increased computational cost; future work will focus on optimizing the algorithm and applying it to real MWA data. |
| title | Bayes-SCF: A Bayesian filter to mitigate foreground leakage in the 21-cm power spectrum |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2603.25543 |