scholarly journals Exploring the origin of the fine structures in the CMB temperature angular power spectrum

2013 ◽  
Vol 87 (2) ◽  
Author(s):  
Kohei Kumazaki ◽  
Kiyotomo Ichiki ◽  
Naoshi Sugiyama ◽  
Joseph Silk
Keyword(s):  
Power Spectrum ◽  
Angular Power Spectrum ◽  
Fine Structures ◽  
Cmb Temperature

scholarly journals Analytical model for CMB temperature angular power spectrum from cosmic (super-)strings

2010 ◽  
Vol 82 (6) ◽  
Author(s):  
Daisuke Yamauchi ◽  
Keitaro Takahashi ◽  
Yuuiti Sendouda ◽  
Chul-Moon Yoo ◽  
Misao Sasaki
Keyword(s):  
Power Spectrum ◽  
Analytical Model ◽  
Angular Power Spectrum ◽  
Cmb Temperature

scholarly journals A Measurement of the Angular Power Spectrum of the CMB Temperature Anisotropy from the 2003 Flight of BOOMERANG

2006 ◽  
Vol 647 (2) ◽  
pp. 823-832 ◽  
Author(s):  
W. C. Jones ◽  
P. A. R. Ade ◽  
J. J. Bock ◽  
J. R. Bond ◽  
J. Borrill ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Temperature Anisotropy ◽  
Angular Power Spectrum ◽  
Cmb Temperature

scholarly journals An Accurate Reconstruction of CMB E Mode Signal over Large Angular Scales using Prior Information of CMB Covariance Matrix in ILC Algorithm

2020 ◽  
Author(s):  
Ujjal Purkayastha ◽  
Vipin Sudevan ◽  
Rajib Saha
Keyword(s):  
Power Spectrum ◽  
Linear Combination ◽  
Covariance Matrix ◽  
Large Scale ◽  
Prior Information ◽  
Future Generation ◽  
Temperature Anisotropy ◽  
Satellite Mission ◽  
Angular Power Spectrum ◽  
Accurate Reconstruction

Abstract Recently, the internal-linear-combination (ILC) method was investigated extensively in the context of reconstruction of Cosmic Microwave Background (CMB) temperature anisotropy signal using observations obtained by WMAP and Planck satellite missions. In this article, we, for the first time, apply the ILC method to reconstruct the large scale CMB E mode polarization signal, which could probe the ionization history, using simulated observations of 15 frequency CMB polarization maps of future generation Cosmic Origin Explorer (COrE) satellite mission. We find that the clean power spectra, from the usual ILC, are strongly biased due to non zero CMB-foregrounds chance correlations. In order to address the issues of bias and errors we extend and improve the usual ILC method for CMB E mode reconstruction by incorporating prior information of theoretical E mode angular power spectrum while estimating the weights for linear combination of input maps (Sudevan & Saha 2018b). Using the E mode covariance matrix effectively suppresses the CMB-foreground chance correlation power leading to an accurate reconstruction of cleaned CMB E mode map and its angular power spectrum. We compare the performance of the usual ILC and the new method over large angular scales and show that the later produces significantly statistically improved results than the former. The new E mode CMB angular power spectrum contains neither any significant negative bias at the low multipoles nor any positive foreground bias at relatively higher mutlipoles. The error estimates of the cleaned spectrum agree very well with the cosmic variance induced error.

scholarly journals Statistical modelling of the cosmological dispersion measure

2021 ◽  
Vol 502 (2) ◽  
pp. 2615-2629
Author(s):  
Ryuichi Takahashi ◽  
Kunihito Ioka ◽  
Asuka Mori ◽  
Koki Funahashi
Keyword(s):  
Dark Matter ◽  
Correlation Function ◽  
Probability Distribution ◽  
Power Spectrum ◽  
Free Electron ◽  
Statistical Modelling ◽  
Dispersion Measure ◽  
Host Galaxies ◽  
Angular Power Spectrum ◽  
Bias Factor

ABSTRACT We have investigated the basic statistics of the cosmological dispersion measure (DM)—such as its mean, variance, probability distribution, angular power spectrum, and correlation function—using the state-of-the-art hydrodynamic simulations, IllustrisTNG300, for the fast radio burst cosmology. To model the DM statistics, we first measured the free-electron abundance and the power spectrum of its spatial fluctuations. The free-electron power spectrum turns out to be consistent with the dark matter power spectrum at large scales, but it is strongly damped at small scales (≲  Mpc) owing to the stellar and active galactic nucleus feedback. The free-electron power spectrum is well modelled using a scale-dependent bias factor (the ratio of its fluctuation amplitude to that of the dark matter). We provide analytical fitting functions for the free-electron abundance and its bias factor. We next constructed mock sky maps of the DM by performing standard ray-tracing simulations with the TNG300 data. The DM statistics are calculated analytically from the fitting functions of the free-electron distribution, which agree well with the simulation results measured from the mock maps. We have also obtained the probability distribution of source redshift for a given DM, which helps in identifying the host galaxies of FRBs from the measured DMs. The angular two-point correlation function of the DM is described by a simple power law, $\xi (\theta) \approx 2400 (\theta /{\rm deg})^{-1} \, {\rm pc}^2 \, {\rm cm}^{-6}$, which we anticipate will be confirmed by future observations when thousands of FRBs are available.

scholarly journals Demonstrating the Tapered Gridded Estimator (TGE) for the Cosmological HI 21-cm Power Spectrum using 150 MHz GMRT observations

2020 ◽  
Author(s):  
Srijita Pal ◽  
Somnath Bharadwaj ◽  
Abhik Ghosh ◽  
Samir Choudhuri
Keyword(s):  
Power Spectrum ◽  
Unbiased Estimate ◽  
Power Spectra ◽  
Neutral Hydrogen ◽  
Statistical Error ◽  
Temperature Fluctuations ◽  
Radio Frequency Interference ◽  
Rectangular Region ◽  
Angular Power Spectrum ◽  
The Mean

Abstract We apply the Tapered Gridded Estimator (TGE) for estimating the cosmological 21-cm power spectrum from 150 MHz GMRT observations which corresponds to the neutral hydrogen (HI) at redshift z = 8.28. Here TGE is used to measure the Multi-frequency Angular Power Spectrum (MAPS) Cℓ(Δν) first, from which we estimate the 21-cm power spectrum P(k⊥, k∥). The data here are much too small for a detection, and the aim is to demonstrate the capabilities of the estimator. We find that the estimated power spectrum is consistent with the expected foreground and noise behaviour. This demonstrates that this estimator correctly estimates the noise bias and subtracts this out to yield an unbiased estimate of the power spectrum. More than $47\%$ of the frequency channels had to be discarded from the data owing to radio-frequency interference, however the estimated power spectrum does not show any artifacts due to missing channels. Finally, we show that it is possible to suppress the foreground contribution by tapering the sky response at large angular separations from the phase center. We combine the k modes within a rectangular region in the ‘EoR window’ to obtain the spherically binned averaged dimensionless power spectra Δ2(k) along with the statistical error σ associated with the measured Δ2(k). The lowest k-bin yields Δ2(k) = (61.47)2 K2 at k = 1.59 Mpc−1, with σ = (27.40)2 K2. We obtain a 2 σ upper limit of (72.66)2 K2 on the mean squared HI 21-cm brightness temperature fluctuations at k = 1.59 Mpc−1.

scholarly journals Validating a novel angular power spectrum estimator using simulated low frequency radio-interferometric data

New Astronomy ◽  
2017 ◽  
Vol 57 ◽  
pp. 94-103 ◽  
Author(s):  
Samir Choudhuri ◽  
Nirupam Roy ◽  
Somnath Bharadwaj ◽  
Sk. Saiyad Ali ◽  
Abhik Ghosh ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Low Frequency ◽  
Angular Power Spectrum

scholarly journals Improved Diffuse Foreground Subtraction with the ILC Method: CMB Map and Angular Power Spectrum UsingPlanckandWMAPObservations

2017 ◽  
Vol 842 (1) ◽  
pp. 62 ◽  
Author(s):  
Vipin Sudevan ◽  
Pavan K. Aluri ◽  
Sarvesh Kumar Yadav ◽  
Rajib Saha ◽  
Tarun Souradeep
Keyword(s):  
Power Spectrum ◽  
Angular Power Spectrum

scholarly journals A comparison of CMB angular power spectrum estimators at large scales: the TT case

2014 ◽  
Vol 440 (2) ◽  
pp. 957-964 ◽  
Author(s):  
D. Molinari ◽  
A. Gruppuso ◽  
G. Polenta ◽  
C. Burigana ◽  
A. De Rosa ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Angular Power Spectrum
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