cosmic microwave background
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2022 ◽  
Vol 2022 (01) ◽  
pp. 001
Sarvesh Kumar Yadav ◽  
Rajib Saha

Abstract In the era of precision cosmology, accurate estimation of cosmological parameters is based upon the implicit assumption of the Gaussian nature of Cosmic Microwave Background (CMB) radiation. Therefore, an important scientific question to ask is whether the observed CMB map is consistent with Gaussian prediction. In this work, we extend previous studies based on CMB spherical harmonic phases (SHP) to examine the validity of the hypothesis that the temperature field of the CMB is consistent with a Gaussian random field (GRF). The null hypothesis is that the corresponding CMB SHP are independent and identically distributed in terms of a uniform distribution in the interval [0, 2π] [1,2]. We devise a new model-independent method where we use ordered and non-parametric Rao's statistic, based on sample arc-lengths to comprehensively test uniformity and independence of SHP for a given ℓ mode and independence of nearby ℓ mode SHP. We performed our analysis on the scales limited by spherical harmonic modes ≤ 128, to restrict ourselves to signal-dominated regions. To find the non-uniform or dependent sets of SHP, we calculate the statistic for the data and 10000 Monte Carlo simulated uniformly random sets of SHP and use 0.05 and 0.001 α levels to distinguish between statistically significant and highly significant detections. We first establish the performance of our method using simulated Gaussian, non-Gaussian CMB temperature maps, along with observed non-Gaussian 100 and 143 GHz Planck channel maps. We find that our method, performs efficiently and accurately in detecting phase correlations generated in all of the non-Gaussian simulations and observed foreground contaminated 100 and 143 GHz Planck channel temperature maps. We apply our method on Planck satellite mission's final released CMB temperature anisotropy maps- COMMANDER, SMICA, NILC, and SEVEM along with WMAP 9 year released ILC map. We report that SHP corresponding to some of the m-modes are non-uniform, some of the ℓ mode SHP and neighboring mode pair SHP are correlated in cleaned CMB maps. The detection of non-uniformity or correlation in the SHP indicates the presence of non-Gaussian signals in the foreground minimized CMB maps.

2021 ◽  
Vol 127 (27) ◽  
A. Ricciardone ◽  
L. Valbusa Dall’Armi ◽  
N. Bartolo ◽  
D. Bertacca ◽  
M. Liguori ◽  

2021 ◽  
Vol 923 (2) ◽  
pp. 153
Fuyu Dong ◽  
Pengjie Zhang ◽  
Le Zhang ◽  
Ji Yao ◽  
Zeyang Sun ◽  

Abstract Low-density points (LDPs), obtained by removing high-density regions of observed galaxies, can trace the large-scale structures (LSSs) of the universe. In particular, it offers an intriguing opportunity to detect weak gravitational lensing from low-density regions. In this work, we investigate the tomographic cross-correlation between Planck cosmic microwave background (CMB) lensing maps and LDP-traced LSSs, where LDPs are constructed from the DR8 data release of the DESI legacy imaging survey, with about 106–107 galaxies. We find that, due to the large sky coverage (20,000 deg2) and large redshift depth (z ≤ 1.2), a significant detection (10σ–30σ) of the CMB lensing–LDP cross-correlation in all six redshift bins can be achieved, with a total significance of ∼53σ over ℓ ≤ 1024. Moreover, the measurements are in good agreement with a theoretical template constructed from our numerical simulation in the WMAP 9 yr ΛCDM cosmology. A scaling factor for the lensing amplitude A lens is constrained to A lens = 1 ± 0.12 for z < 0.2, A lens = 1.07 ± 0.07 for 0.2 < z < 0.4, and A lens = 1.07 ± 0.05 for 0.4 < z < 0.6, with the r-band absolute magnitude cut of −21.5 for LDP selection. A variety of tests have been performed to check the detection reliability against variations in LDP samples and galaxy magnitude cuts, masks, CMB lensing maps, multipole ℓ cuts, sky regions, and photo-z bias. We also perform a cross-correlation measurement between CMB lensing and galaxy number density, which is consistent with the CMB lensing–LDP cross-correlation. This work therefore further convincingly demonstrates that LDP is a competitive tracer of LSS.

2021 ◽  
Vol 29 (1) ◽  
Paul Shah ◽  
Pablo Lemos ◽  
Ofer Lahav

AbstractSince the expansion of the universe was first established by Edwin Hubble and Georges Lemaître about a century ago, the Hubble constant $$H_0$$ H 0 which measures its rate has been of great interest to astronomers. Besides being interesting in its own right, few properties of the universe can be deduced without it. In the last decade, a significant gap has emerged between different methods of measuring it, some anchored in the nearby universe, others at cosmological distances. The SH0ES team has found $$H_0 = 73.2 \pm 1.3 \; \;\,\hbox {kms}^{-1} \,\hbox {Mpc}^{-1}$$ H 0 = 73.2 ± 1.3 kms - 1 Mpc - 1 locally, whereas the value found for the early universe by the Planck Collaboration is $$H_0 = 67.4 \pm 0.5 \; \;\,\hbox {kms}^{-1} \,\hbox {Mpc}^{-1}$$ H 0 = 67.4 ± 0.5 kms - 1 Mpc - 1 from measurements of the cosmic microwave background. Is this gap a sign that the well-established $${\varLambda} {\text{CDM}}$$ Λ CDM cosmological model is somehow incomplete? Or are there unknown systematics? And more practically, how should humble astronomers pick between competing claims if they need to assume a value for a certain purpose? In this article, we review results and what changes to the cosmological model could be needed to accommodate them all. For astronomers in a hurry, we provide a buyer’s guide to the results, and make recommendations.

2021 ◽  
Vol 923 (1) ◽  
pp. 96
N. Gupta ◽  
C. L. Reichardt

Abstract We present a new application of deep learning to reconstruct the cosmic microwave background (CMB) temperature maps from images of the microwave sky and to use these reconstructed maps to estimate the masses of galaxy clusters. We use a feed-forward deep-learning network, mResUNet, for both steps of the analysis. The first deep-learning model, mResUNet-I, is trained to reconstruct foreground and noise-suppressed CMB maps from a set of simulated images of the microwave sky that include signals from the CMB, astrophysical foregrounds like dusty and radio galaxies, instrumental noise as well as the cluster’s own thermal Sunyaev–Zel’dovich signal. The second deep-learning model, mResUNet-II, is trained to estimate cluster masses from the gravitational-lensing signature in the reconstructed foreground and noise-suppressed CMB maps. For SPTpol-like noise levels, the trained mResUNet-II model recovers the mass for 104 galaxy cluster samples with a 1σ uncertainty Δ M 200 c est / M 200 c est = 0.108 and 0.016 for input cluster mass M 200 c true = 10 14 M ⊙ and 8 × 1014 M ⊙, respectively. We also test for potential bias on recovered masses, finding that for a set of 105 clusters the estimator recovers M 200 c est = 2.02 × 10 14 M ⊙ , consistent with the input at 1% level. The 2σ upper limit on potential bias is at 3.5% level.

2021 ◽  
Vol 922 (2) ◽  
pp. 259
M. Millea ◽  
C. M. Daley ◽  
T-L. Chou ◽  
E. Anderes ◽  
P. A. R. Ade ◽  

Abstract We perform the first simultaneous Bayesian parameter inference and optimal reconstruction of the gravitational lensing of the cosmic microwave background (CMB), using 100 deg2 of polarization observations from the SPTpol receiver on the South Pole Telescope. These data reach noise levels as low as 5.8 μK arcmin in polarization, which are low enough that the typically used quadratic estimator (QE) technique for analyzing CMB lensing is significantly suboptimal. Conversely, the Bayesian procedure extracts all lensing information from the data and is optimal at any noise level. We infer the amplitude of the gravitational lensing potential to be A ϕ = 0.949 ± 0.122 using the Bayesian pipeline, consistent with our QE pipeline result, but with 17% smaller error bars. The Bayesian analysis also provides a simple way to account for systematic uncertainties, performing a similar job as frequentist “bias hardening” or linear bias correction, and reducing the systematic uncertainty on A ϕ due to polarization calibration from almost half of the statistical error to effectively zero. Finally, we jointly constrain A ϕ along with A L, the amplitude of lensing-like effects on the CMB power spectra, demonstrating that the Bayesian method can be used to easily infer parameters both from an optimal lensing reconstruction and from the delensed CMB, while exactly accounting for the correlation between the two. These results demonstrate the feasibility of the Bayesian approach on real data, and pave the way for future analysis of deep CMB polarization measurements with SPT-3G, Simons Observatory, and CMB-S4, where improvements relative to the QE can reach 1.5 times tighter constraints on A ϕ and seven times lower effective lensing reconstruction noise.

2021 ◽  
Vol 923 (2) ◽  
pp. 212
Satadru Bag ◽  
Varun Sahni ◽  
Arman Shafieloo ◽  
Yuri Shtanov

Abstract Braneworld models with induced gravity exhibit phantom-like behavior of the effective equation of state of dark energy. They can, therefore, naturally accommodate higher values of H 0, preferred by recent local measurements while satisfying the cosmic microwave background constraints. We test the background evolution in such phantom braneworld scenarios with the current observational data sets. We find that the phantom braneworld prefers a higher value of H 0 even without the R19 prior, thereby providing a much better fit to the local measurements. Although this braneworld model cannot fully satisfy all combinations of cosmological observables, among existing dark energy candidates the phantom brane provides one of the most compelling explanations of cosmic evolution.

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