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Register a new userA Monte Carlo comparison between template-based and Wiener-filter CMB dipole estimators
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We review and compare two different CMB dipole estimators discussed in the literature, and assess their performances through Monte Carlo simulations. The first method amounts to simple template regression with partial sky data, while the second method is an optimal Wiener filter (or Gibbs sampling) implementation. The main difference between the two methods is that the latter approach takes into account correlations with higher-order CMB temperature fluctuations that arise from non-orthogonal spherical harmonics on an incomplete sky, which for recent CMB data sets (such as Planck) is the dominant source of uncertainty. For an accepted sky fraction of 81% and an angular CMB power spectrum corresponding to the best-fit Planck 2018 $Lambda$CDM model, we find that the uncertainty on the recovered dipole amplitude is about six times smaller for the Wiener filter approach than for the template approach, corresponding to 0.5 and 3$~mu$K, respectively. Similar relative differences are found for the corresponding directional parameters and other sky fractions. We note that the Wiener filter algorithm is generally applicable to any dipole estimation problem on an incomplete sky, as long as a statistical and computationally tractable model is available for the unmasked higher-order fluctuations. The methodology described in this paper forms the numerical basis for the most recent determination of the CMB solar dipole from Planck, as summarized by arXiv:2007.04997.
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In this paper we continue our study of CMB TE cross correlation as a source of information about primordial gravitational waves. In an accompanying paper, we considered the zero multipole method. In this paper we use Wiener filtering of the CMB TE data to remove the density perturbation contribution to the TE power spectrum. In principle this leaves only the contribution of PGWs. We examine two toy experiments (one ideal and one more realistic), to see how well they constrain PGWs using the TE power spectrum. We consider three tests applied to a combination of observational data and data sets generated by Monte Carlo simulations: (1) Signal-to-Noise test, (2) sign test, and (3) Wilcoxon rank sum test. We compare these tests with each other and with the zero multipole method. Finally, we compare the signal-to-noise ratio of TE correlation measurements first with corresponding signal-to-noise ratios for BB ground based measurements and later with current and future TE correlation space measurements. We found that an ideal TE correlation experiment limited only by cosmic variance can detect PGWs with a tensor-to-scalar ratio $r=0.3$ at 98% confidence level with the $S/N$ test, 93% confidence level with the sign test, and 80% confidence level for the Wilcoxon rank sum test. We also compare all results with corresponding results obtained using the zero multipole method. We demonstrate that to measure PGWs by their contribution to the TE cross correlation power spectrum in a realistic ground based experiment when real instrumental noise is taken into account, the tensor-to-scalar ratio, $r$, must be approximately four times larger. In the sense to detect PGWs, the zero multipole method is the best, next best is the S/N test, then the sign test, and the worst is the Wilcoxon rank sum test.
Many problems in machine learning and statistics involve nested expectations and thus do not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest estimators, such that terms in an outer estimator themselves involve calculation of a separate, nested, estimation. We investigate the statistical implications of nesting MC estimators, including cases of multiple levels of nesting, and establish the conditions under which they converge. We derive corresponding rates of convergence and provide empirical evidence that these rates are observed in practice. We further establish a number of pitfalls that can arise from naive nesting of MC estimators, provide guidelines about how these can be avoided, and lay out novel methods for reformulating certain classes of nested expectation problems into single expectations, leading to improved convergence rates. We demonstrate the applicability of our work by using our results to develop a new estimator for discrete Bayesian experimental design problems and derive error bounds for a class of variational objectives.
In the context of cosmic microwave background (CMB) data analysis, we compare the efficiency at large scale of two angular power spectrum algorithms, implementing, respectively, the quadratic maximum likelihood (QML) estimator and the pseudo spectrum (pseudo-Cl) estimator. By exploiting 1000 realistic Monte Carlo (MC) simulations, we find that the QML approach is markedly superior in the range l=[2-100]. At the largest angular scales, e.g. l < 10, the variance of the QML is almost 1/3 (1/2) that of the pseudo-Cl, when we consider the WMAP kq85 (kq85 enlarged by 8 degrees) mask, making the pseudo spectrum estimator a very poor option. Even at multipoles l=[20-60], where pseudo-Cl methods are traditionally used to feed the CMB likelihood algorithms, we find an efficiency loss of about 20%, when we considered the WMAP kq85 mask, and of about 15% for the kq85 mask enlarged by 8 degrees. This should be taken into account when claiming accurate results based on pseudo-Cl methods. Some examples concerning typical large scale estimators are provided.
We use Bayesian model selection techniques to test extensions of the standard flat LambdaCDM paradigm. Dark-energy and curvature scenarios, and primordial perturbation models are considered. To that end, we calculate the Bayesian evidence in favour of each model using Population Monte Carlo (PMC), a new adaptive sampling technique which was recently applied in a cosmological context. The Bayesian evidence is immediately available from the PMC sample used for parameter estimation without further computational effort, and it comes with an associated error evaluation. Besides, it provides an unbiased estimator of the evidence after any fixed number of iterations and it is naturally parallelizable, in contrast with MCMC and nested sampling methods. By comparison with analytical predictions for simulated data, we show that our results obtained with PMC are reliable and robust. The variability in the evidence evaluation and the stability for various cases are estimated both from simulations and from data. For the cases we consider, the log-evidence is calculated with a precision of better than 0.08. Using a combined set of recent CMB, SNIa and BAO data, we find inconclusive evidence between flat LambdaCDM and simple dark-energy models. A curved Universe is moderately to strongly disfavoured with respect to a flat cosmology. Using physically well-motivated priors within the slow-roll approximation of inflation, we find a weak preference for a running spectral index. A Harrison-Zeldovich spectrum is weakly disfavoured. With the current data, tensor modes are not detected; the large prior volume on the tensor-to-scalar ratio r results in moderate evidence in favour of r=0. [Abridged]
We describe the details of the binned bispectrum estimator as used for the official 2013 and 2015 analyses of the temperature and polarization CMB maps from the ESA Planck satellite. The defining aspect of this estimator is the determination of a map bispectrum (3-point correlator) that has been binned in harmonic space. For a parametric determination of the non-Gaussianity in the map (the so-called fNL parameters), one takes the inner product of this binned bispectrum with theoretically motivated templates. However, as a complementary approach one can also smooth the binned bispectrum using a variable smoothing scale in order to suppress noise and make coherent features stand out above the noise. This allows one to look in a model-independent way for any statistically significant bispectral signal. This approach is useful for characterizing the bispectral shape of the galactic foreground emission, for which a theoretical prediction of the bispectral anisotropy is lacking, and for detecting a serendipitous primordial signal, for which a theoretical template has not yet been put forth. Both the template-based and the non-parametric approaches are described in this paper.
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