Cosmic Microwave Background Signal Research Articles

Spectro-spatial evolution of the CMB. Part III. Transfer functions, power spectra and Fisher forecasts

In this paper, we provide the first computations for the distortion transfer functions of the cosmic microwave background (CMB) in the perturbed Universe, following up on paper I and II in this series. We illustrate the physical effects inherent to the solutions, discussing and demonstrating various limiting cases for the perturbed photon spectrum. We clarify the relationship between distortion transfer functions and the photon spectrum itself, providing the machinery that can then compute constrainable CMB signal power spectra including spectral distortions for single energy injection and decaying particle scenarios. Our results show that the μ × T and y × T power spectra reach levels that can be constrained with current and future CMB experiments without violating existing constraints from COBE/FIRAS. The amplitude of the cross-correlation signal directly depends on the average distortion level, therefore establishing a novel fundamental link between the state of the primordial plasma from redshift 103 ≲ z ≲ 3 × 106 and the frequency-dependent CMB sky. This providesa new method to constrain average early energy release using CMB imagers. As an example we derive constraints on single energy release and decaying particle scenarios. This shows that LiteBIRD may be able to improve the energy release limits of COBE/FIRAS by up to a factor of ≃ 2.5, while PICO could tighten the constraints by more than one order of magnitude. The signals considered here could furthermore provide a significant challenge to reaching cosmic variance-limited constraints on primordial non-Gaussianity from distortion anisotropy studies. Our work further highlights the immense potential for a synergistic spectroscopic approach to future CMB measurements and analyses.

Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach

The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ deep learning as a data-driven approach to CMB cleaning from multi-frequency full-sky maps. In particular, we develop a graph-based Bayesian convolutional neural network based on the U-Net architecture that predicts cleaned CMB with pixel-wise uncertainty estimates. We demonstrate the potential of this technique on realistic simulated data based on the Planck mission. We show that our model ac- accurately recovers the cleaned CMB sky map and resulting angular power spectrum while identifying regions of uncertainty. Finally, we discuss the current challenges and the path forward for deploying our model for CMB recovery on real observations.

Optimal method for reconstructing polychromatic maps from broadband observations with an asymmetric antenna pattern

Broadband time-ordered data obtained from telescopes with a wavelength-dependent, asymmetric beam pattern can be used to extract maps at multiple wavelengths from a single scan. This technique is especially useful when collecting data on cosmic phenomena such as the cosmic microwave background (CMB) radiation, as it provides the ability to separate the CMB signal from foreground contaminants. We develop a method to determine the optimal linear combinations of wavelengths (``colors'') that can be reconstructed for a given telescope design and the number of colors that are measurable with high signal-to-noise ratio. The optimal colors are found as eigenvectors of a matrix derived from the inverse noise covariance matrix. When the telescope is able to scan the sky isotropically, it is useful to transform to a spherical harmonic basis, in which this matrix has a particularly simple form. We propose using the optimal colors determined from the isotropic case even when the actual scanning pattern is not isotropic (e.g., covers only part of the sky). We perform simulations showing that maps in multiple colors can be reconstructed accurately from both full-sky and partial-sky scans. Although the original motivation for this research comes from mapping the CMB, this method of polychromatic mapmaking will have broader applications throughout astrophysics.

Constraining the polarisation flux density and angle of point sources by training a convolutional neural network

Context. Constraining the polarisation properties of extragalactic point sources is a relevant task not only because they are one of the main contaminants for primordial cosmic microwave background B-mode detection if the tensor-to-scalar ratio is lower than r = 0.001, but also for a better understanding of the properties of radio-loud active galactic nuclei. Aims. We develop and train a machine learning model based on a convolutional neural network to learn how to estimate the polarisation flux density and angle of point sources embedded in cosmic microwave background images knowing only their positions. Methods. To train the neural network, we used realistic simulations of patches of 32 × 32 pixels in area at the 217 GHz Planck channel with injected point sources at their centres. The patches also contain a realistic background composed of the cosmic microwave background signal, the Galactic thermal dust, and instrumental noise. We split our analysis into three parts: firstly, we studied the comparison between true and estimated polarisation flux densities for P, Q, and U simulations. Secondly, we analysed the comparison between true and estimated polarisation angles. Finally, we studied the performance of our model with the 217 GHz Planck map and compared our results against the detected sources of the Second Planck Catalogue of Compact Sources (PCCS2). Results. We find that our model can be used to reliably constrain the polarisation flux density of sources above the 80 mJy level. For this limit, we obtain relative errors of lower than 30% in most of the flux density levels. Training the same network with Q and U maps, the reliability limit is above ±250 mJy when determining the polarisation angle of both Q and U sources. Above that cut, the network can constrain angles with a 1σ uncertainty of ±29° and ±32° for Q and U sources, respectively. We test this neural network against real data from the 217 GHz Planck channel, obtaining similar results to the PCCS2 for some sources; although we also find discrepancies in the 300–400mJy flux density range with respect to the Planck catalogue. Conclusions. Based on these results, our model appears to be a promising tool for estimating the polarisation flux densities and angles of point sources above 80 mJy in any catalogue with very small computational time requirements.

Robustness of cosmic birefringence measurement against Galactic foreground emission and instrumental systematics

The polarization of the cosmic microwave background (CMB) can be used to search for parity-violating processes like that predicted by a Chern-Simons coupling to a light pseudoscalar field. Such an interaction rotates E modes into E modes in the observed CMB signal through an effect known as cosmic birefringence. Even though isotropic birefringence can be confused with the rotation produced by a miscalibration of the detectors' polarization angles, the degeneracy between both effects is broken when Galactic foreground emission is used as a calibrator. In this work, we use realistic simulations of the High-Frequency Instrument of the Planck mission to test the impact that Galactic foreground emission and instrumental systematics have on the recent birefringence measurements obtained through this technique. Our results demonstrate the robustness of the methodology against the miscalibration of polarization angles and other systematic effects, like intensity-to-polarization leakage, beam leakage, or cross-polarization effects. However, our estimator is sensitive to the EB correlation of polarized foreground emission. Here we propose to correct the bias induced by dust EB by modeling the foreground signal with templates produced in Bayesian component-separation analyses that fit parametric models to CMB data. Acknowledging the limitations of currently available dust templates like that of the Commander sky model, high-precision CMB data and a characterization of dust beyond the modified blackbody paradigm are needed to obtain a definitive measurement of cosmic birefringence in the future.

CENN: A fully convolutional neural network for CMB recovery in realistic microwave sky simulations

Context. Component separation is the process with which emission sources in astrophysical maps are generally extracted by taking multi-frequency information into account. It is crucial to develop more reliable methods for component separation for future cosmic microwave background (CMB) experiments such as the Simons Observatory, the CMB-S4, or the LiteBIRD satellite. Aims. We aim to develop a machine learning method based on fully convolutional neural networks called the CMB extraction neural network (CENN) in order to extract the CMB signal in total intensity by training the network with realistic simulations. The frequencies we used are the Planck channels 143, 217, and 353 GHz, and we validated the neural network throughout the sky and at three latitude intervals: 0° < |b| < 5°, 5° < |b| < 30°, and 30° < |b| < 90°, Moreover, we used neither Galactic nor point-source (PS) masks. Methods. To train the neural network, we produced multi-frequency realistic simulations in the form of patches of 256 × 256 pixels that contained the CMB signal, the Galactic thermal dust, cosmic infrared background, and PS emissions, the thermal Sunyaev–Zel’dovich effect from galaxy clusters, and instrumental noise. After validating the network, we compared the power spectra from input and output maps. We analysed the power spectrum from the residuals at each latitude interval and throughout the sky, and we studied how our model handled high contamination at small scales. Results. We obtained a CMB power spectrum with a mean difference between input and output of 13 ± 113 µK2 for multipoles up to above 4000. We computed the residuals, obtaining 700 ± 60 µK2 for 0° < |b| < 5°, 80 ± 30 µK2 for 5° < |b| < 30°, and 30 ± 20 µK2 for 30° < |b| < 90° for multipoles up to above 4000. For the entire sky, we obtained 30 ± 10 µK2 for l ≤ 1000 and 20 ± 10 µK2 for l ≤ 4000. We validated the neural network in a single patch with strong contamination at small scales, obtaining a difference between input and output of 50 ± 120 µK2 and residuals of 40 ± 10 µK2 up to l ~ 2500. In all cases, the uncertainty of each measure was taken as the standard deviation. Conclusions. The results show that fully convolutional neural networks are promising methods for performing component separation in future CMB experiments. Moreover, we show that CENN is reliable against different levels of contamination from Galactic and PS foregrounds at both large and small scales.

Recovering the CMB Signal with Machine Learning

The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious foreground contaminations from several sources, such as Galactic synchrotron and thermal dust emissions. Here, we build a deep convolutional neural network (CNN) to recover the tiny CMB signal from various huge foreground contaminations. Focusing on CMB temperature fluctuations, we find that the CNN model can successfully recover the CMB temperature maps with high accuracy, and that the deviation of the recovered power spectrum C ℓ is smaller than the cosmic variance at ℓ > 10. We then apply this method to the current Planck observations, and find that the recovered CMB is quite consistent with that disclosed by the Planck Collaboration, which indicates that the CNN method can provide a promising approach to the component separation of CMB observations. Furthermore, we test the CNN method with simulated CMB polarization maps based on the CMB-S4 experiment. The result shows that both the EE and BB power spectra can be recovered with high accuracy. Therefore, this method will be helpful for the detection of primordial gravitational waves in current and future CMB experiments. The CNN is designed to analyze two-dimensional images, thus this method is not only able to process full-sky maps, but also partial-sky maps. Therefore, it can also be used for other similar experiments, such as radio surveys like the Square Kilometer Array.

Moment expansion of polarized dust SED: A new path towards capturing the CMB B-modes with LiteBIRD

Accurate characterization of the polarized dust emission from our Galaxy will be decisive in the quest for the cosmic microwave background (CMB) primordial B-modes. An incomplete modeling of its potentially complex spectral properties could lead to biases in the CMB polarization analyses and to a spurious measurement of the tensor-to-scalar ratio r. It is particularly crucial for future surveys like the LiteBIRD satellite, the goal of which is to constrain the faint primordial signal leftover by inflation with an accuracy on the tensor-to-scalar ratio r of the order of 10−3. Variations of the dust properties along and between lines of sight lead to unavoidable distortions of the spectral energy distribution (SED) that cannot be easily anticipated by standard component-separation methods. This issue can be tackled using a moment expansion of the dust SED, an innovative parametrization method imposing minimal assumptions on the sky complexity. In the present paper, we apply this formalism to the B-mode cross-angular power spectra computed from simulated LiteBIRD polarization data at frequencies between 100 and 402 GHz that contain CMB, dust, and instrumental noise. The spatial variation of the dust spectral parameters (spectral index β and temperature T) in our simulations lead to significant biases on r (∼21 σr) if not properly taken into account. Performing the moment expansion in β, as in previous studies, reduces the bias but does not lead to sufficiently reliable estimates of r. We introduce, for the first time, the expansion of the cross-angular power spectra SED in both β and T, showing that, at the sensitivity of LiteBIRD, the SED complexity due to temperature variations needs to be taken into account in order to prevent analysis biases on r. Thanks to this expansion, and despite the existing correlations between some of the dust moments and the CMB signal responsible for a rise in the error on r, we can measure an unbiased value of the tensor-to-scalar ratio with a dispersion as low as σr = 8.8 × 10−4.

Multi-frequency point source detection with fully convolutional networks: Performance in realistic microwave sky simulations

Context. Point source (PS) detection is an important issue for future cosmic microwave background (CMB) experiments since they are one of the main contaminants to the recovery of CMB signal on small scales. Improving its multi-frequency detection would allow us to take into account valuable information otherwise neglected when extracting PS using a channel-by-channel approach. Aims. We aim to develop an artificial intelligence method based on fully convolutional neural networks to detect PS in multi-frequency realistic simulations and compare its performance against one of the most popular multi-frequency PS detection methods, the matrix filters. The frequencies used in our analysis are 143, 217, and 353 GHz, and we imposed a Galactic cut of 30°. Methods. We produced multi-frequency realistic simulations of the sky by adding contaminating signals to the PS maps as the CMB, the cosmic infrared background, the Galactic thermal emission, the thermal Sunyaev-Zel’dovich effect, and the instrumental and PS shot noises. These simulations were used to train two neural networks called flat and spectral MultiPoSeIDoNs. The first one considers PS with a flat spectrum, and the second one is more realistic and general because it takes into account the spectral behaviour of the PS. Then, we compared the performance on reliability, completeness, and flux density estimation accuracy for both MultiPoSeIDoNs and the matrix filters. Results. Using a flux detection limit of 60 mJy, MultiPoSeIDoN successfully recovered PS reaching the 90% completeness level at 58 mJy for the flat case, and at 79, 71, and 60 mJy for the spectral case at 143, 217, and 353 GHz, respectively. The matrix filters reach the 90% completeness level at 84, 79, and 123 mJy. To reduce the number of spurious sources, we used a safer 4σ flux density detection limit for the matrix filters, the same as was used in the Planck catalogues, obtaining the 90% of completeness level at 113, 92, and 398 mJy. In all cases, MultiPoSeIDoN obtains a much lower number of spurious sources with respect to the filtering method. The recovering of the flux density of the detections, attending to the results on photometry, is better for the neural networks, which have a relative error of 10% above 100 mJy for the three frequencies, while the filter obtains a 10% relative error above 150 mJy for 143 and 217 GHz, and above 200 mJy for 353 GHz. Conclusions. Based on the results, neural networks are the perfect candidates to substitute filtering methods to detect multi-frequency PS in future CMB experiments. Moreover, we show that a multi-frequency approach can detect sources with higher accuracy than single-frequency approaches also based on neural networks.

An unbiased estimator of the full-sky CMB angular power spectrum at large scales using neural networks

ABSTRACT Accurate estimation of the cosmic microwave background (CMB) angular power spectrum is enticing due to the prospect for precision cosmology it presents. Galactic foreground emissions, however, contaminate the CMB signal and need to be subtracted reliably in order to lessen systematic errors on the CMB temperature estimates. Typically, bright foregrounds in a region lead to further uncertainty in temperature estimates in the area even after some foreground removal technique is performed and hence determining the underlying full-sky angular power spectrum poses a challenge. We explore the feasibility of utilizing artificial neural networks to predict the angular power spectrum of the full-sky CMB temperature maps from the observed angular power spectrum of the partial sky in which CMB temperatures in some bright foreground regions are masked. We present our analysis at large angular scales with two different masks. We produce unbiased predictions of the full-sky angular power spectrum and recover the underlying theoretical power spectrum using neural networks. Our predictions are also uncorrelated to a large extent. We further show that the multipole-space covariances of the predictions of full-sky spectra made by the artificial neural networks are much smaller than those of the estimates obtained using the pseudo-Cℓ method.

Point source detection with fully convolutional networks

Context. Point sources are one of the main contaminants to the recovery of the cosmic microwave background signal at small scales, and their careful detection will be important for the next generation of cosmic microwave background experiments like LiteBird. Aims. We want to develop a method based on fully convolutional networks to detect sources in realistic simulations, and to compare its performance against one of the most used point source detection method in this context, the Mexican hat wavelet 2 (MHW2). The frequencies for our analysis are the 143, 217, and 353 GHz Planck channels. Methods. We produce realistic simulations of point sources at each frequency taking into account potential contaminating signals as the cosmic microwave background, the cosmic infrared background, the Galactic thermal emission, the thermal Sunyaev-Zel’dovich effect, and the instrumental and point source shot noises. We first produce a set of training simulations at 217 GHz to train the neural network that we named PoSeIDoN. Then we apply both PoSeIDoN and the MHW2 to recover the point sources in the validating simulations at all the frequencies, comparing the results by estimating the reliability, completeness, and flux density estimation accuracy. Moreover, the receiver operating characteristic (ROC) curves are computed in order to asses the methods’performance. Results. In the extra-galactic region with a 30° galactic cut, the neural network successfully recovers point sources at 90% completeness corresponding to 253, 126, and 250 mJy for 143, 217, and 353 GHz respectively. In the same validation simulations the wavelet with a 3σ flux density detection limit recovers point sources up to 181, 102, and 153 mJy at 90% completeness. To reduce the number of spurious sources, we also apply a safer 4σ flux density detection limit, the same as in the Planck catalogues, increasing the 90% completeness levels: 235, 137, and 192 mJy. In all cases PoSeIDoN produces a much lower number of spurious sources with respect to MHW2. As expected, the results on spurious sources for both techniques worsen when reducing the galactic cut to 10°. Conclusions. Our results suggest that using neural networks is a very promising approach for detecting point sources using data from cosmic microwave background experiments, providing overall better results in dealing with spurious sources with respect to the more usual filtering approaches. Moreover, PoSeIDoN gives competitive results even at the 217 GHz nearby channels where the network was not trained.

Requirements for future CMB satellite missions: photometric and band-pass response calibration

Current and future Cosmic Microwave Background (CMB) Radiation experiments are targeting the polarized B-mode signal. The small amplitude of this signal makes a successful measurement challenging for current technologies. Therefore, very accurate studies to mitigate and control possible systematic effects are vital to achieve a successful observation. An additional challenge is coming from the presence of polarized Galactic foreground signals that contaminate the CMB signal. When they are combined, the foreground signals dominate the polarized CMB signal at almost every relevant frequency. Future experiments, like the LiteBIRD space-borne mission, aim at measuring the CMB B-mode signal with high accuracy to measure the tensor-to-scalar ratio r at the 10−3 level. We present a method to study the photometric calibration requirement needed to minimize the leakage of polarized Galactic foreground signals into CMB polarization maps for a multi-frequency CMB experiment. We applied this method to the LiteBIRD case, and we found precision requirements for the photometric calibration in the range ∼10−4–2.5×10−3 depending on the frequency band. Under the assumption that the detectors are uncorrelated, we found requirements per detector in the range ∼0.18×10−2–2.0×10−2. Finally, we relate the calibration requirements to the band-pass resolution to define constraints for a few representative band-pass responses: Δν∼0.2–2 GHz.

Cleaning our own dust: simulating and separating galactic dust foregrounds with neural networks

ABSTRACT Separating galactic foreground emission from maps of the cosmic microwave background (CMB) and quantifying the uncertainty in the CMB maps due to errors in foreground separation are important for avoiding biases in scientific conclusions. Our ability to quantify such uncertainty is limited by our lack of a model for the statistical distribution of the foreground emission. Here, we use a deep convolutional generative adversarial network (DCGAN) to create an effective non-Gaussian statistical model for intensity of emission by interstellar dust. For training data we use a set of dust maps inferred from observations by the Planck satellite. A DCGAN is uniquely suited for such unsupervised learning tasks as it can learn to model a complex non-Gaussian distribution directly from examples. We then use these simulations to train a second neural network to estimate the underlying CMB signal from dust-contaminated maps. We discuss other potential uses for the trained DCGAN, and the generalization to polarized emission from both dust and synchrotron.

On the Impact of Random Residual Calibration Error on the Gibbs ILC CMB Estimates over Large Angular Scales

Abstract Residual errors in calibration coefficients corresponding to observed cosmic microwave background (CMB) maps are an important issue when estimating a pure CMB signal. These errors in the input-foreground-contaminated CMB maps, if not properly taken into account in a component separation method, may lead to bias in the cleaned CMB map and estimated CMB angular power spectrum. But the inability to exactly determine the calibration coefficients corresponding to each observed CMB map from a multifrequency CMB experiment makes it very difficult to incorporate their exact and actual values during the component separation method. Hence, the effect of any random and residual calibration error in the cleaned CMB map and its angular power spectrum of a component separation problem can only be understood by performing detailed Monte Carlo simulations. In this paper, we investigate the impact of using input-observed CMB maps with random calibration errors on the posterior density of a cleaned CMB map and theoretical CMB angular power spectrum over large angular scales of the sky following the Gibbs Internal-Linear-Combination (ILC) method. By performing detailed Monte Carlo simulations of WMAP and Planck temperature anisotropy observations, including their estimate on calibration errors, we show that the best-fit map corresponding to the posterior maximum is minimally biased in the Gibbs ILC method by a CMB normalization bias and residual foreground bias. The residual calibration-induced error in the best-fit power spectrum causes an overall 6% increase of the net error when added in quadrature with the cosmic-variance-induced error.

Likelihood Methods for CMB Experiments

A great deal of experimental effort is currently being devoted to the precise measurements of the cosmic microwave background (CMB) sky in temperature and polarisation. Satellites, balloon-borne, and ground-based experiments scrutinize the CMB sky at multiple scales, and therefore enable to investigate not only the evolution of the early Universe, but also its late-time physics with unprecedented accuracy. The pipeline leading from time ordered data as collected by the instrument to the final product is highly structured. Moreover, it has also to provide accurate estimates of statistical and systematic uncertainties connected to the specific experiment. In this paper, we review likelihood approaches targeted to the analysis of the CMB signal at different scales, and to the estimation of key cosmological parameters. We consider methods that analyze the data in the spatial (i.e., pixel-based) or harmonic domain. We highlight the most relevant aspects of each approach and compare their performance.

Mitigating Complex Dust Foregrounds in Future Cosmic Microwave Background Polarization Experiments

Abstract Polarized Galactic foregrounds are one of the primary sources of systematic error in measurements of the B-mode polarization of the cosmic microwave background (CMB). Experiments are becoming increasingly sensitive to complexities in the foreground frequency spectra that are not captured by standard parametric models, potentially affecting our ability to efficiently separate out these components. Employing a suite of dust models encompassing a variety of physical effects, we simulate observations of a future seven-band CMB experiment to assess the impact of these complexities on parametric component separation. We identify configurations of frequency bands that minimize the “model errors” caused by fitting simple parametric models to more complex “true” foreground spectra, which bias the inferred CMB signal. We find that: (a) fits employing a simple two-parameter modified blackbody (MBB) dust model tend to produce significant bias in the recovered polarized CMB signal in the presence of physically realistic dust foregrounds; (b) generalized MBB models with three additional parameters reduce this bias in most cases, but non-negligible biases can remain, and can be hard to detect; (c) line-of-sight effects, which give rise to frequency decorrelation, and the presence of iron grains are the most problematic complexities in the dust emission for recovering the true CMB signal. More sophisticated simulations will be needed to demonstrate that future CMB experiments can successfully mitigate these more physically realistic dust foregrounds.

Bandpass mismatch error for satellite CMB experiments I: estimating the spurious signal

Future Cosmic Microwave Background (CMB) satellite missions aim to use the B mode polarization to measure the tensor-to-scalar ratio r with a sensitivity σr ≲ 10−3. Achieving this goal will not only require sufficient detector array sensitivity but also unprecedented control of all systematic errors inherent in CMB polarization measurements. Since polarization measurements derive from differences between observations at different times and from different sensors, detector response mismatches introduce leakages from intensity to polarization and thus lead to a spurious B mode signal. Because the expected primordial B mode polarization signal is dwarfed by the known unpolarized intensity signal, such leakages could contribute substantially to the final error budget for measuring r. Using simulations we estimate the magnitude and angular spectrum of the spurious B mode signal resulting from bandpass mismatch between different detectors. It is assumed here that the detectors are calibrated, for example using the CMB dipole, so that their sensitivity to the primordial CMB signal has been perfectly matched. Consequently the mismatch in the frequency bandpass shape between detectors introduces differences in the relative calibration of galactic emission components. We simulate this effect using a range of scanning patterns being considered for future satellite missions. We find that the spurious contribution to r from the reionization bump on large angular scales (ℓ < 10) is ≈ 10−3 assuming large detector arrays and 20 percent of the sky masked. We show how the amplitude of the leakage depends on the nonuniformity of the angular coverage in each pixel that results from the scan pattern.

Semianalytic calculation of cosmic microwave background anisotropies from wiggly and superconducting cosmic strings

We study how the presence of world-sheet currents affects the evolution of cosmic string networks, and their impact on predictions for the cosmic microwave background (CMB) anisotropies generated by these networks. We provide a general description of string networks with currents and explicitly investigate in detail two physically motivated examples: wiggly and superconducting cosmic string networks. By using a modified version of the CMBact code, we show quantitatively how the relevant network parameters in both of these cases influence the predicted CMB signal. Our analysis suggests that previous studies have overestimated the amplitude of the anisotropies for wiggly strings. For superconducting strings the amplitude of the anisotropies depends on parameters which presently are not well known - but which can be measured in future high-resolution numerical simulations.

Can bigravity gravitational baryogenesis explain the CMB 143 GHz excess line?

It has been recently claimed [arXiv: 1510.00126], that the 143 GHz excess line in the Cosmic MicrowaveBackground (CMB) spectrumcould be explained by a collision of our Universe with an alternate Universe in which the baryon to entropy ratio is 65 times larger than the corresponding value measured for ourUniverse. This 143 GHz excess line is due to baryons, as was claimed, since the excess line corresponds to the recombination epoch, while the rest of the CMB signal is free of such excess lines. Thus the excess line is ascribed to an effect of collision of our Universe with a parallel universe. In this paper, we propose an alternative mechanism to explain the 143 GHz excess CMB line by using a simple bimetric gravity model which makes use of two metrics, the foreground metric g μν and the background metric f μν. The foreground Universe describes our Universe, and the background Universe is assumed to be underlying. The metrics are chosen to satisfy \({f_{\mu \nu }} = D_{{g_{\mu \nu }}}^2\), and the bimetric gravity model is constrained in such a way that the resulting Einstein equations for the background and foreground Universe are identical. In effect, the foreground and background Universe are indistinguishable at the cosmological solutions level. However, for the choices of the metrics we made, the scalar curvatures of the foreground and background Universes, namely R g and R f, are related by R f = 1/D 2, which can effectively result in different baryon to entropy ratios for the two Universes, via the gravitational baryogenesis mechanism. According to the gravitational baryogenesis mechanism, the baryon to entropy ratio is \(\eta B = \dot R/M_*^2T\), which means that the baryon to entropy ratio for the foreground and the background Universes we chose, namely ηB(g) and ηB(f), satisfy the relation \(\eta B\left( f \right) = \frac{1}{{{D^2}}}\eta B\left( g \right)\), and if D is chosen as \(D = 1/\sqrt {65} \), this could explain the 143 GHz excess line. The resulting phenomenological picture is quite appealing, since in the context of our bimetric gravity model, the foreground and background Universes coexist and are indistinguishable at the cosmological solutions level, but they are distinguishable only via the gravitational baryogenesis mechanism, which results in a baryon to entropy ratio for the background Universe, which is 65 time larger from the one corresponding to our Universe.

Making maps of cosmic microwave background polarization for B-mode studies: the POLARBEAR example

Analysis of cosmic microwave background (CMB) datasets typically requires some filtering of the raw time-ordered data. For instance, in the context of ground-based observations, filtering is frequently used to minimize the impact of low frequency noise, atmospheric contributions and/or scan synchronous signals on the resulting maps. In this work we have explicitly constructed a general filtering operator, which can unambiguously remove any set of unwanted modes in the data, and then amend the map-making procedure in order to incorporate and correct for it. We show that such an approach is mathematically equivalent to the solution of a problem in which the sky signal and unwanted modes are estimated simultaneously and the latter are marginalized over. We investigated the conditions under which this amended map-making procedure can render an unbiased estimate of the sky signal in realistic circumstances. We then discuss the potential implications of these observations on the choice of map-making and power spectrum estimation approaches in the context of B-mode polarization studies. Specifically, we have studied the effects of time-domain filtering on the noise correlation structure in the map domain, as well as impact it may haveon the performance of the popular pseudo-spectrum estimators. We conclude that although maps produced by the proposed estimators arguably provide the most faithful representation of the sky possible given the data, they may not straightforwardly lead to the best constraints on the power spectra of the underlying sky signal and special care may need to be taken to ensure this is the case. By contrast, simplified map-makers which do not explicitly correct for time-domain filtering, but leave it to subsequent steps in the data analysis, may perform equally well and be easier and faster to implement. We focused on polarization-sensitive measurements targeting the B-mode component of the CMB signal and apply the proposed methods to realistic simulations based on characteristics of an actual CMB polarization experiment, POLARBEAR. Our analysis and conclusions are however more generally applicable.