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Register a new userConfusion noise due to clustered extragalactic point sources. Application of logarithmic cumulants for parameter estimation
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The calculation of the characteristic function of the signal fluctuations due to clustered astrophysical sources is performed in this paper. For the typical case of power-law differential number counts and two-point angular correlation function, we present an extension of Zolotarevs theorem that allows us to compute the cumulants of the logarithm of the absolute value of the intensity. As a test, simulations based on recent observations of radio galaxies are then carried out, showing that these cumulants can be very useful for determining the fundamental parameters defining the number counts and the correlation. If the angular correlation scale of the observed source population is known, the method presented here is able to obtain estimators of the amplitude and slope of the power-law number counts with mean absolute errors that are one order of magnitude better than previous techniques, that did not take into account the correlation. Even if the scale of correlation is not well known, the method is able to estimate it and still performs much better than if the effect of correlations is not considered.
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We have derived the relationship between polarization and intensity fluctuations due to point sources. In the case of a Poisson distribution of a population with uniform evolution properties and constant polarization degree, polarization fluctuations are simply equal to intensity fluctuations times the average polarization degree. Conservative estimates of the polarization degree of the classes of extragalactic sources contributing to fluctuations in the frequency ranges covered by the forthcoming space missions MAP and Planck Surveyor indicate that extragalactic sources will not be a strong limiting factor to measurements of the polarization of the Cosmic Microwave Background.
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. We present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in CosmoSIS, including CAMB, Planck, cosmic shear calculations, and a suite of samplers. We illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis
We construct a Bayesian inference deep learning machine for parameter estimation of gravitational wave events of binaries of black hole coalescence. The structure of our deep Bayseian machine adopts the conditional variational autoencoder scheme by conditioning both the gravitational wave strains and the variations of amplitude spectral density of the detector noise. We show that our deep Bayesian machine is capable of yielding the posteriors compatible with the ones from the nest sampling method, and of fighting against the noise outliers. We also apply our deep Bayesian machine to the LIGO/Virgo O3 events, and find that conditioning detector noise to fight against its drifting is relevant for the events with medium signal-to-noise ratios.
We consider a set of M images, whose pixel intensities at a common point can be treated as the components of a M-dimensional vector. We are interested in the estimation of the modulus of such a vector associated to a compact source. For instance, the detection/estimation of the polarized signal of compact sources immersed in a noisy background is relevant in some fields like Astrophysics. We develop two different techniques, one based on the Maximum Likelihood Estimator (MLE) applied to the modulus distribution, the modulus filter (ModF) and other based on prefiltering the components before fusion, the filtered fusion (FF), to deal with this problem. We present both methods in the general case of M images and apply them to the particular case of three images (linear plus circular polarization). Numerical simulations have been performed to test these filters considering polarized compact sources immersed in stationary noise. The FF performs better than the ModF in terms of errors in the estimated amplitude and position of the source, especially in the low signal-to-noise case. We also compare both methods with the direct application of a matched filter (MF) on the polarization data. This last technique is clearly outperformed by the new methods.
The estimation of the polarization $P$ of extragalactic compact sources in Cosmic Microwave Background images is a very important task in order to clean these images for cosmological purposes -- as, for example, to constrain the tensor-to-scalar ratio of primordial fluctuations during inflation -- and also to obtain relevant astrophysical information about the compact sources themselves in a frequency range, $ u sim 10$--$200$ GHz, where observations have only very recently started to be available. In this paper we propose a Bayesian maximum a posteriori (MAP) approach estimation scheme which incorporates prior information about the distribution of the polarization fraction of extragalactic compact sources between 1 and 100 GHz. We apply this Bayesian scheme to white noise simulations and to more realistic simulations that include CMB intensity, Galactic foregrounds and instrumental noise with the characteristics of the QUIJOTE experiment Wide Survey at 11 GHz. Using these simulations, we also compare our Bayesian method with the frequentist Filtered Fusion method that has been already used in WMAP data and in the emph{Planck} mission. We find that the Bayesian method allows us to decrease the threshold for a feasible estimation of $P$ to levels below $sim 100$ mJy (as compared to $sim 500$ mJy that was the equivalent threshold for the frequentist Filtered Fusion). We compare the bias introduced by the Bayesian method and find it to be small in absolute terms. Finally, we test the robustness of the Bayesian estimator against uncertainties in the prior and in the flux density of the sources. We find that the Bayesian estimator is robust against moderate changes in the parameters of the prior and almost insensitive to realistic errors in the estimated photometry of the sources.
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