arXiv subjects

Benjamin D. Wandelt

Publications and source records attributed to Benjamin D. Wandelt.

At least 91 records · Page 5Linked to original sources

arXiv2020

A new probe of Axion-Like Particles: CMB polarization distortions due to cluster magnetic fields

We propose using the upcoming Cosmic Microwave Background (CMB) ground based experiments to detect the signal of ALPs (Axion like particles) interacting with magnetic fields in galaxy clusters. The conversion between CMB photons and ALPs in the presence of the cluster magnetic field can cause a polarized spectral distortion in the CMB around a galaxy cluster. The strength of the signal depends upon the redshift of the galaxy cluster and will exhibit a distinctive spatial profile around it depending upon the structure of electron density and magnetic field. This distortion produces a different shape from the other known spectral distortions like $y$-type and $μ$-type and hence are separable from the multi-frequency CMB observation. The spectrum is close to kinematic Sunyaev-Zeldovich (kSZ) signal but can be separated from it using the polarization information. For the future ground-based CMB experiments such as Simons Observatory and CMB-S4, we estimate the measurability of this signal in the presence of foreground contamination, instrument noise and CMB anisotropies. This new avenue can probe the photon-ALP coupling over the ALP mass range from $10^{-13}$ eV to $10^{-12}$ eV with two orders of magnitude better accuracy from CMB-S4 than the current existing bounds.

astro-ph.CO↗

arXiv2020

Bayesian delensing delight: sampling-based inference of the primordial CMB and gravitational lensing

The search for primordial gravitational waves in the Cosmic Microwave Background (CMB) will soon be limited by our ability to remove the lensing contamination to $B$-mode polarization. The often-used quadratic estimator for lensing is known to be suboptimal for surveys that are currently operating and will continue to become less and less efficient as instrumental noise decreases. While foregrounds can in principle be mitigated by observing in more frequency bands, progress in delensing hinges entirely on algorithmic advances. We demonstrate here a new inference method that solves this problem by sampling the exact Bayesian posterior of any desired cosmological parameters, of the gravitational lensing potential, and of the delensed CMB maps, given lensed temperature and polarization data. We validate the method using simulated CMB data with non-white noise and masking on up to 650\,deg$^2$ patches of sky. A unique strength of this approach is the ability to jointly estimate cosmological parameters which control both the primordial CMB and the lensing potential, which we demonstrate here for the first time by sampling both the tensor-to-scalar ratio, $r$, and the amplitude of the lensing potential, $A_ϕ$. The method allows us to perform the most precise check to-date of several important approximations underlying CMB-S4 $r$ forecasting, and we confirm these yield the correct expected uncertainty on $r$ to better than 10%.

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arXiv2019

The void halo mass function: a promising probe of neutrino mass

Cosmic voids, the underdense regions in the universe, are particularly sensitive to diffuse density components such as cosmic neutrinos. This sensitivity is enhanced by the match between void sizes and the free-streaming scale of massive neutrinos. Using the massive neutrino simulations \texttt{MassiveNuS}, we investigate the effect of neutrino mass on dark matter halos as a function of environment. We find that the halo mass function depends strongly on neutrino mass and that this dependence is more pronounced in voids than in high-density environments. An observational program that measured the characteristic mass of the most massive halos in voids should be able to place novel constraints on the sum of the masses of neutrinos $\sum m_ν$. The neutrino mass effect in the simulations is quite strong: In a 512$^3$ $h^{-3}$ Mpc$^3$ survey, the mean mass of the 1000 most massive halos in the void interiors is $(4.82 \pm 0.11) \times 10^{12} h^{-1}M_{\odot}$ for $\sum m_ν= 0.6$ eV and $(8.21 \pm 0.13) \times 10^{12} h^{-1}M_{\odot}$ for $\sum m_ν= 0.1$ eV. Subaru (SuMIRe), Euclid and WFIRST will have both spectroscopic and weak lensing surveys. Covering volumes at least 50 times larger than our simulations, they should be sensitive probes of neutrino mass through void substructure.

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arXiv2019

Wiener filtering and pure E/B decomposition of CMB maps with anisotropic correlated noise

We present an augmented version of our dual messenger algorithm for spin field reconstruction on the sphere, while accounting for highly non-trivial and realistic noise models such as modulated correlated noise. We also describe an optimization method for the estimation of noise covariance from Monte Carlo simulations. Using simulated Planck polarized cosmic microwave background (CMB) maps as a showcase, we demonstrate the capabilities of the algorithm in reconstructing pure E and B maps, guaranteed to be free from ambiguous modes resulting from the leakage or coupling issue that plagues conventional methods of E/B separation. Due to its high speed execution, coupled with lenient memory requirements, the algorithm can be optimized in exact global Bayesian analyses of state-of-the-art CMB data for a statistically optimal separation of pure E and B modes. Our algorithm, therefore, has a potentially key role in the data analysis of high-resolution and high-sensitivity CMB data, especially with the range of upcoming CMB experiments tailored for the detection of the elusive primordial B-mode signal.

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arXiv2019

Neural physical engines for inferring the halo mass distribution function

An ambitious goal in cosmology is to forward-model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be done is necessarily limited. Consequently, one needs a mapping between the density of dark matter averaged over ~Mpc scales, and the distribution of dark matter halos (used as a proxy for galaxies) in the same region. Here we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model. The method is based on the Bayesian analysis of simple, physically motivated, neural network-like architectures, which we denote as neural physical engines, and neural density estimation. As a result, we are able to sample the initial phases of the dark matter density field whilst inferring the parameters describing the halo mass distribution function, providing a fully Bayesian interpretation of both the initial dark matter density distribution and the neural bias model. We successfully run an upgraded BORG inference using our new likelihood and neural bias model with halo catalogues derived from full N-body simulations. We notice orders of magnitude improvement in modelling compared to classical biasing techniques.

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arXiv2019

Joint Bayesian analysis of large angular scale CMB temperature anomalies

Cosmic microwave background measurements show an agreement with the concordance cosmology model except for a few notable anomalies: Power Suppression, the lack of large scale power in the temperature data compared to what is expected in the concordance model, and Cosmic Hemispherical Asymmetry, a dipolar breakdown of statistical isotropy. An expansion of the CMB covariance in Bipolar Spherical Harmonics naturally parametrizes both these large-scale anomalies, allowing us to perform an exhaustive, fully Bayesian joint analysis of the power spectrum and violations of statistical isotropy up to the dipole level. Our analysis sheds light on the scale dependence of the Cosmic Hemispherical Asymmetry. Assuming a scale-dependent dipole modulation model with a two-parameter power law form, we explore the posterior pdf of amplitude $A(l = 16)$ and the power law index $α$ and find the maximum a posteriori values $A_*(l = 16) = 0.064 \pm 0.022$ and $α_* = -0.92 \pm 0.22$. The maximum a posteriori direction associated with the Cosmic Hemispherical Asymmetry is $(l,b) = (247.8^o, -19.6^o)$ in Galactic coordinates, consistent with previous analyses. We evaluate the Bayes factor $B_{SI-DM}$ to compare the Cosmic Hemispherical Asymmetry model with the isotropic model. The data prefer but do not substantially favor the anisotropic model ($B_{SI-DM}=0.4$). We consider several priors and find that this evidence ratio is robust to prior choice. The large-scale power suppression does not soften when jointly inferring both the isotropic power spectrum and the parameters of the asymmetric model, indicating no evidence that these anomalies are coupled.

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arXiv2019

Cosmic Shear: Inference from Forward Models

Density-estimation likelihood-free inference (DELFI) has recently been proposed as an efficient method for simulation-based cosmological parameter inference. Compared to the standard likelihood-based Markov Chain Monte Carlo (MCMC) approach, DELFI has several advantages: it is highly parallelizable, there is no need to assume a possibly incorrect functional form for the likelihood and complicated effects (e.g the mask and detector systematics) are easier to handle with forward models. In light of this, we present two DELFI pipelines to perform weak lensing parameter inference with lognormal realizations of the tomographic shear field -- using the C_l summary statistic. The first pipeline accounts for the non-Gaussianities of the shear field, intrinsic alignments and photometric-redshift error. We validate that it is accurate enough for Stage III experiments and estimate that O(1000) simulations are needed to perform inference on Stage IV data. By comparing the second DELFI pipeline, which makes no assumption about the functional form of the likelihood, with the standard MCMC approach, which assumes a Gaussian likelihood, we test the impact of the Gaussian likelihood approximation in the MCMC analysis. We find it has a negligible impact on Stage IV parameter constraints. Our pipeline is a step towards seamlessly propagating all data-processing, instrumental, theoretical and astrophysical systematics through to the final parameter constraints.

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arXiv2019

Massive Neutrinos Leave Fingerprints on Cosmic Voids

Do void statistics contain information beyond the tracer 2-point correlation function? Yes! As we vary the sum of the neutrino masses, we find void statistics contain information absent when using just tracer 2-point statistics. Massive neutrinos uniquely affect cosmic voids. We explore their impact on void clustering using both the DEMNUni and MassiveNuS simulations. For voids, neutrino effects depend on the observed void tracers. As the neutrino mass increases, the number of small voids traced by cold dark matter particles increases and the number of large voids decreases. Surprisingly, when massive, highly biased, halos are used as tracers, we find the opposite effect. The scale at which voids cluster, as well as the void correlation, is similarly sensitive to the sum of neutrino masses and the tracers. This scale dependent trend is not due to simulation volume or halo density. The interplay of these signatures in the void abundance and clustering leaves a distinct fingerprint that could be detected with observations and potentially help break degeneracies between different cosmological parameters. This paper paves the way to exploit cosmic voids in future surveys to constrain the mass of neutrinos.

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arXiv2019

Constraints on non-resonant photon-axion conversion from the Planck satellite data

The non-resonant conversion of Cosmic Microwave Background (CMB) photons into scalar as well as light pseudoscalar particles such as axion-like particles (ALPs) in the presence of turbulent magnetic fields can cause a unique, spatially fluctuating spectral distortion in the CMB. We use the publicly available Planck temperature maps for the frequency channels (70-545 GHz) to obtain the first ALP distortion map using $45\%$ clean part of the sky. The $95^{th}$ percentile upper limit on the RMS fluctuation of ALP distortions from the cleanest part of the CMB sky at $15$ arcmin angular resolution is $18.5 \times 10^{-6}$. The RMS fluctuation in the distortion map is also consistent with different combinations of frequency channels and sky-fractions.

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arXiv2019

Signatures of Cosmic Reionization on the 21cm 2- and 3-point Correlation Function I: Quadratic Bias Modeling

The three-point correlation function (3PCF) of the 21cm brightness temperature from the Epoch of Reionization (EoR) probes complementary information to the commonly studied two-point correlation function (2PCF) about the morphology of ionized regions. We investigate the 21cm 2PCF and 3PCF in configuration space using semi-numerical simulations and test whether they can be described by the local quadratic bias model. We find that fits of bias model predictions for the 2PCF and 3PCF deviate from our measurements by $\sim 20\%$ at scales above the typical size of ionized regions ($\simeq 30$ Mpc) and at early times with global neutral fractions of $\langle x_{\rm HI} \rangle \gtrsim 0.7$. At later times and smaller scales these deviations increase strongly, indicating a break down of the bias model. The 2PCF and 3PCF fits of the linear bias parameter agree at the $10\%$ level for different EoR model configurations. This agreement holds, when adding redshift space distortions to the simulations. The relation between spatial fluctuations in the matter density and the 21cm signal, as predicted by the bias model, is consistent with direct measurements of this relation in simulations for large smoothing scales ($\gtrsim 30$ Mpc). From this latter test we conclude that negative amplitudes of the 21cm 3PCF result from negative bias parameters, which describe the anti-correlation between the matter over-densities and the 21cm signal during the EoR. However, a more detailed interpretation of the bias parameters may require a description of non-local contributions to the bias model.

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arXiv2019

Cosmic voids: a novel probe to shed light on our Universe

Cosmic voids, the less dense patches of the Universe, are promising laboratories to extract cosmological information. Thanks to their unique low density character, voids are extremely sensitive to diffuse components such as neutrinos and dark energy, and represent ideal environments to study modifications of gravity, where the effects of such modifications are expected to be more prominent. Robust void-related observables, including for example redshift-space distortions (RSD) and weak lensing around voids, are a promising way to chase and test new physics. Cosmological analysis of the large-scale structure of the Universe predominantly relies on the high density regions. Current and upcoming surveys are designed to optimize the extraction of cosmological information from these zones, but leave voids under-exploited. A dense, large area spectroscopic survey with imaging capabilities is ideal to exploit the power of voids fully. Besides helping illuminate the nature of dark energy, modified gravity, and neutrinos, this survey will give access to a detailed map of under-dense regions, providing an unprecedented opportunity to observe and study a so far under-explored galaxy population.

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arXiv2018

Cosmological inference from Bayesian forward modelling of deep galaxy redshift surveys

We present a large-scale Bayesian inference framework to constrain cosmological parameters using galaxy redshift surveys, via an application of the Alcock-Paczyński (AP) test. Our physical model of the non-linearly evolved density field, as probed by galaxy surveys, employs Lagrangian perturbation theory (LPT) to connect Gaussian initial conditions to the final density field, followed by a coordinate transformation to obtain the redshift space representation for comparison with data. We generate realizations of primordial and present-day matter fluctuations given a set of observations. This hierarchical approach encodes a novel AP test, extracting several orders of magnitude more information from the cosmological expansion compared to classical approaches, to infer cosmological parameters and jointly reconstruct the underlying 3D dark matter density field. The novelty of this AP test lies in constraining the comoving-redshift transformation to infer the appropriate cosmology which yields isotropic correlations of the galaxy density field, with the underlying assumption relying purely on the cosmological principle. Such an AP test does not rely explicitly on modelling the full statistics of the field. We verify in depth via simulations that this renders our test robust to model misspecification. This leads to another crucial advantage, namely that the cosmological parameters exhibit extremely weak dependence on the currently unresolved phenomenon of galaxy bias, thereby circumventing a potentially key limitation. This is consequently among the first methods to extract a large fraction of information from statistics other than that of direct density contrast correlations, without being sensitive to the amplitude of density fluctuations. We perform several statistical efficiency and consistency tests on a mock galaxy catalogue, using the SDSS-III survey as template.

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arXiv2018

Beyond the classical distance-redshift test: cross-correlating redshift-free standard candles and sirens with redshift surveys

LSST will supply up to $10^6$ supernovae (SNe) to constrain dark energy through the distance-redshift ($D_L$-$z$) test. Obtaining spectroscopic SN redshifts (spec-$z$s) is unfeasible; alternatives are suboptimal and may be biased. We propose a powerful multi-tracer generalization of the Alcock-Paczynski test that pairs redshift-free distance tracers and an overlapping galaxy redshift survey. Cross-correlating $5\times 10^4$ redshift-free SNe with DESI or Euclid outperforms the classical $D_L$-$z$ test with spec-$z$s for all SN. Our method also applies to gravitational wave sirens or any redshift-free distance tracer.

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arXiv2018

Optimal proposals for Approximate Bayesian Computation

We derive the optimal proposal density for Approximate Bayesian Computation (ABC) using Sequential Monte Carlo (SMC) (or Population Monte Carlo, PMC). The criterion for optimality is that the SMC/PMC-ABC sampler maximise the effective number of samples per parameter proposal. The optimal proposal density represents the optimal trade-off between favoring high acceptance rate and reducing the variance of the importance weights of accepted samples. We discuss two convenient approximations of this proposal and show that the optimal proposal density gives a significant boost in the expected sampling efficiency compared to standard kernels that are in common use in the ABC literature, especially as the number of parameters increases.

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arXiv2018

Automatic physical inference with information maximising neural networks

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find non-linear functionals of data that maximise Fisher information: information maximising neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise; inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra; and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

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arXiv2018

Polarized anisotropic spectral distortions of the CMB: Galactic and extragalactic constraints on photon-axion conversion

We revisit the cosmological constraints on resonant and non-resonant conversion of photons to axions in the cosmological magnetic fields. We find that the constraints on photon-axion coupling and primordial magnetic fields are much weaker than previously claimed for low mass axion like particles with masses $m_{\rm a} \lesssim 5\times 10^{-13}\,\text{eV}$. In particular we find that the axion mass range $10^{-14}\,\text{eV} \le m_{\rm a} \le 5\times 10^{-13}\,\text{eV}$ is not excluded by the CMB data contrary to the previous claims. We also examine the photon-axion conversion in the Galactic magnetic fields. Resonant conversion in the large scale coherent Galactic magnetic field results in $100\%$ polarized anisotropic spectral distortions of the CMB for the mass range $10^{-13}\,\text{eV} \lesssim m_{\rm a} \lesssim 10^{-11}\,\text{eV}$. The polarization pattern traces the transverse to line of sight component of the Galactic magnetic field while both the anisotropy in the Galactic magnetic field and electron distribution imprint a characteristic anisotropy pattern in the spectral distortion. Our results apply to scalar as well as pseudoscalar particles. For conversion to scalar particles, the polarization is rotated by $90^{\circ}$ allowing us to distinguish them from the pseudoscalars. For $m_{\rm a} \lesssim 10^{-14} \,\text{eV}$ we have non-resonant conversion in the small scale turbulent magnetic field of the Galaxy resulting in anisotropic but unpolarized spectral distortion in the CMB. These unique signatures are potential discriminants against the isotropic and non-polarized signals such as primary CMB, and $μ$ and $y$ distortions with the anisotropic nature making it accessible to experiments with only relative calibration like Planck, LiteBIRD, and CORE. We forecast for PIXIE as well as for these experiments using Fisher matrix formalism.

astro-ph.CO↗

arXiv2018

ARKCoS: Artifact-Suppressed Accelerated Radial Kernel Convolution on the Sphere

We describe a hybrid Fourier/direct space convolution algorithm for compact radial (azimuthally symmetric) kernels on the sphere. For high resolution maps covering a large fraction of the sky, our implementation takes advantage of the inexpensive massive parallelism afforded by consumer graphics processing units (GPUs). Applications involve modeling of instrumental beam shapes in terms of compact kernels, computation of fine-scale wavelet transformations, and optimal filtering for the detection of point sources. Our algorithm works for any pixelization where pixels are grouped into isolatitude rings. Even for kernels that are not bandwidth limited, ringing features are completely absent on an ECP grid. We demonstrate that they can be highly suppressed on the popular HEALPix pixelization, for which we develop a freely available implementation of the algorithm. As an example application, we show that running on a high-end consumer graphics card our method speeds up beam convolution for simulations of a characteristic Planck high frequency instrument channel by two orders of magnitude compared to the commonly used HEALPix implementation on one CPU core while maintaining at typical a fractional RMS accuracy of about 1 part in 10^5.

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arXiv2018

Optimal and fast E/B separation with a dual messenger field

We adapt our recently proposed dual messenger algorithm for spin field reconstruction and showcase its efficiency and effectiveness in Wiener filtering polarized cosmic microwave background (CMB) maps. Unlike conventional preconditioned conjugate gradient (PCG) solvers, our preconditioner-free technique can deal with high-resolution joint temperature and polarization maps with inhomogeneous noise distributions and arbitrary mask geometries with relative ease. Various convergence diagnostics illustrate the high quality of the dual messenger reconstruction. In contrast, the PCG implementation fails to converge to a reasonable solution for the specific problem considered. The implementation of the dual messenger method is straightforward and guarantees numerical stability and convergence. We show how the algorithm can be modified to generate fluctuation maps, which, combined with the Wiener filter solution, yield unbiased constrained signal realizations, consistent with observed data. This algorithm presents a pathway to exact global analyses of high-resolution and high-sensitivity CMB data for a statistically optimal separation of E and B modes. It is therefore relevant for current and next-generation CMB experiments, in the quest for the elusive primordial B-mode signal.

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