2021
DOI: 10.1088/1475-7516/2021/10/081
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Primordial Power Spectrum reconstruction from CMB Weak Lensing Power Spectrum

Abstract: We use the modified and improved Richardson-Lucy (IRL) deconvolution algorithm to reconstruct the Primordial Power Spectrum (PPS) from the Weak Lensing Power Spectrum CL ϕϕ reconstructed from CMB anisotropies. This provides an independent window to observe and constrain the PPS PR (k) along different k scales as compared to CMB Temperature Power Spectrum. The Weak Lensing Power Spectrum does not contain secondary variations in power and hence is cleaner, u… Show more

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Cited by 5 publications
(6 citation statements)
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References 46 publications
(53 reference statements)
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“…Finally, the CMB lensing power spectrum is not very sensitive to this class of primordial features with high frequencies [40]. The power spectrum of the lensing potential is an integrated quantity where a large range of wavenumbers contribute to each multipole; as a result, high frequency oscillations as those considered here are efficiently smoothed in CMB lensing compared to CMB temperature and polarization spectra.…”
Section: Jcap10(2022)083 2 Primordial Oscillatory Features With a Gau...mentioning
confidence: 76%
“…Finally, the CMB lensing power spectrum is not very sensitive to this class of primordial features with high frequencies [40]. The power spectrum of the lensing potential is an integrated quantity where a large range of wavenumbers contribute to each multipole; as a result, high frequency oscillations as those considered here are efficiently smoothed in CMB lensing compared to CMB temperature and polarization spectra.…”
Section: Jcap10(2022)083 2 Primordial Oscillatory Features With a Gau...mentioning
confidence: 76%
“…We expect to further optimize the algorithm by implementing the sparsity algorithm developed in our previous work [18], as well as carry out a full covariance matrix calculation for the reconstructed P R (k) as well as test existing C T T data for features of interest given the newfound accuracy of the NIRL estimator and it's ability to discriminate finely from features vs lensing artefacts without any prior assumptions.…”
Section: Discussionmentioning
confidence: 99%
“…While practical and efficient with respect to computing resources, the issue with this approach is that the template based delensing process inherently assumes a fiducial C φφ L weak lensing power spectrum. It has been demonstrated earlier that the weak lensing power spectrum C φφ L can be expressed as a convolution of the primordial power spectrum P R (k) with the transport kernel G φφ L (k) and the RL estimator has been successfully employed in deconvolution with relevant statistical analyses and optimization algorithms [18]. This naturally poses a circular problem of having to assume a form of C φφ L , based on a Power Law P R (k), when we are trying to reconstruct the P R (k) itself.…”
Section: Temperature Power Spectrum C T T Weak Lensing Correctionmentioning
confidence: 99%
See 1 more Smart Citation
“…Localised features have been studied using wavelet basis functions [52,53]. A direct inversion using Singular Value Decomposition on the transfer kernel [54] and Modified Richardson-Lucy algorithm (MRL) [55][56][57][58][59][60][61][62][63][64] can also be grouped into this category.…”
Section: Jcap03(2024)056mentioning
confidence: 99%