arXiv subjects

Benjamin D. Wandelt

Publications and source records attributed to Benjamin D. Wandelt.

At least 55 records · Page 3Linked to original sources

arXiv2022

The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues

We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological parameter constraints using mock dark matter halo catalogues. We employ Information Maximising Neural Networks (IMNNs) to quantify Fisher information extraction as a function of graph representation. We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference. We reduce the area of joint $Ω_m, σ_8$ parameter constraints with small ($\sim$100 object) halo catalogues by a factor of 42 over the two-point correlation function, and demonstrate that the networks automatically combine mass and clustering information. This work utilises a new IMNN implementation over graph data in Jax, which can take advantage of either numerical or auto-differentiability. We also show that graph IMNNs successfully compress simulations away from the fiducial model at which the network is fitted, indicating a promising alternative to n-point statistics in catalogue simulation-based analyses.

astro-ph.CO↗

arXiv2022

Quijote PNG: The information content of the halo power spectrum and bispectrum

We investigate how much can be learnt about four types of primordial non-Gaussianity (PNG) from small-scale measurements of the halo field. Using the QUIJOTE-PNG simulations, we quantify the information content accessible with measurements of the halo power spectrum monopole and quadrupole, the matter power spectrum, the halo-matter cross spectrum and the halo bispectrum monopole. This analysis is the first to include small, non-linear scales, up to $k_\mathrm{max}=0.5 \mathrm{h/Mpc}$, and to explore whether these scales can break degeneracies with cosmological and nuisance parameters making use of thousands of N-body simulations. We perform all the halo measurements in redshift space with a single sample comprised of all halos with mass $>3.2 \times 10^{13}~h^{-1}M_\odot$. For local PNG, measurements of the scale dependent bias effect from the power spectrum using sample variance cancellation provide significantly tighter constraints than measurements of the halo bispectrum. In this case measurements of the small scales add minimal additional constraining power. In contrast, the information on equilateral and orthogonal PNG is primarily accessible through the bispectrum. For these shapes, small scale measurements increase the constraining power of the halo bispectrum by up to $\times4$, though the addition of scales beyond $k\approx 0.3 \mathrm{h/Mpc}$ improves constraints largely through reducing degeneracies between PNG and the other parameters. These degeneracies are even more powerfully mitigated through combining power spectrum and bispectrum measurements. However even with combined measurements and small scale information, equilateral non-Gaussianity remains highly degenerate with $σ_8$ and our bias model.

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arXiv2022

Inflation: Theory and Observations

Cosmic inflation provides a window to the highest energy densities accessible in nature, far beyond those achievable in any realistic terrestrial experiment. Theoretical insights into the inflationary era and its observational probes may therefore shed unique light on the physical laws underlying our universe. This white paper describes our current theoretical understanding of the inflationary era, with a focus on the statistical properties of primordial fluctuations. In particular, we survey observational targets for three important signatures of inflation: primordial gravitational waves, primordial non-Gaussianity and primordial features. With the requisite advancements in analysis techniques, the tremendous increase in the raw sensitivities of upcoming and planned surveys will translate to leaps in our understanding of the inflationary paradigm and could open new frontiers for cosmology and particle physics. The combination of future theoretical and observational developments therefore offer the potential for a dramatic discovery about the nature of cosmic acceleration in the very early universe and physics on the smallest scales.

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arXiv2022

Perfectly parallel cosmological simulations using spatial comoving Lagrangian acceleration

Existing cosmological simulation methods lack a high degree of parallelism due to the long-range nature of the gravitational force, which limits the size of simulations that can be run at high resolution. To solve this problem, we propose a new, perfectly parallel approach to simulate cosmic structure formation, which is based on the spatial COmoving Lagrangian Acceleration (sCOLA) framework. Building upon a hybrid analytical and numerical description of particles' trajectories, our algorithm allows for an efficient tiling of a cosmological volume, where the dynamics within each tile is computed independently. As a consequence, the degree of parallelism is equal to the number of tiles. We optimised the accuracy of sCOLA through the use of a buffer region around tiles and of appropriate Dirichlet boundary conditions around sCOLA boxes. As a result, we show that cosmological simulations at the degree of accuracy required for the analysis of the next generation of surveys can be run in drastically reduced wall-clock times and with very low memory requirements. The perfect scalability of our algorithm unlocks profoundly new possibilities for computing larger cosmological simulations at high resolution, taking advantage of a variety of hardware architectures.

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arXiv2022

Snowmass Theory Frontier: Astrophysics and Cosmology

We summarize progress made in theoretical astrophysics and cosmology over the past decade and areas of interest for the coming decade. This Report is prepared as the TF09 "Astrophysics and Cosmology" topical group summary for the Theory Frontier as part of the Snowmass 2021 process.

hep-ph↗

arXiv2022

Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum

The first measurements of the 21 cm brightness temperature power spectrum from the epoch of reionization will very likely be achieved in the near future by radio interferometric array experiments such as the Hydrogen Epoch of Reionization Array (HERA) and the Square Kilometre Array (SKA). Standard MCMC analyses use an explicit likelihood approximation to infer the reionization parameters from the 21 cm power spectrum. In this paper, we present a new Bayesian inference of the reionization parameters where the likelihood is implicitly defined through forward simulations using density estimation likelihood-free inference (DELFI). Realistic effects including thermal noise and foreground avoidance are also applied to the mock observations from the HERA and SKA. We demonstrate that this method recovers accurate posterior distributions for the reionization parameters, and outperforms the standard MCMC analysis in terms of the location and size of credible parameter regions. With the minutes-level processing time once the network is trained, this technique is a promising approach for the scientific interpretation of future 21 cm power spectrum observation data. Our code 21cmDELFI-PS is publicly available at this link.

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arXiv2022

Quijote-PNG: Quasi-maximum likelihood estimation of Primordial Non-Gaussianity in the non-linear dark matter density field

Future Large Scale Structure surveys are expected to improve over current bounds on primordial non-Gaussianity (PNG), with a significant impact on our understanding of early Universe physics. The level of such improvements will however strongly depend on the extent to which late time non-linearities erase the PNG signal on small scales. In this work, we show how much primordial information remains in the bispectrum of the non-linear dark matter density field by implementing a new, simulation-based, methodology for joint estimation of PNG amplitudes ($f_{\rm NL}$) and standard $Λ$CDM parameters. The estimator is based on optimally compressed statistics, which, for a given input density field, combine power spectrum and modal bispectrum measurements, and numerically evaluate their covariance and their response to changes in cosmological parameters. We train and validate the estimator using a large suite of N-body simulations (QUIJOTE-PNG), including different types of PNG (local, equilateral, orthogonal). We explicitly test the estimator's unbiasedness, optimality and stability with respect to changes in the total number of input realizations. While the dark matter power spectrum itself contains negligible PNG information, as expected, including it as an ancillary statistic increases the PNG information content extracted from the bispectrum by a factor of order $2$. As a result, we prove the capability of our approach to optimally extract PNG information on non-linear scales beyond the perturbative regime, up to $k_{\rm max} = 0.5~h\,{\rm Mpc}^{-1}$, obtaining marginalized $1$-$σ$ bounds of $Δf_{\rm NL}^{\rm local} \sim 16$, $Δf_{\rm NL}^{\rm equil} \sim 77$ and $Δf_{\rm NL}^{\rm ortho} \sim 40$ on a cubic volume of $1~(\mathrm{Gpc}/h)^3$ at $z=1$. At the same time, we discuss the significant information on cosmological parameters contained on these scales.

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arXiv2022

CARPool Covariance: Fast, unbiased covariance estimation for large-scale structure observables

The covariance matrix $\boldsymbolΣ$ of non-linear clustering statistics that are measured in current and upcoming surveys is of fundamental interest for comparing cosmological theory and data and a crucial ingredient for the likelihood approximations underlying widely used parameter inference and forecasting methods. The extreme number of simulations needed to estimate $\boldsymbolΣ$ to sufficient accuracy poses a severe challenge. Approximating $\boldsymbolΣ$ using inexpensive but biased surrogates introduces model error with respect to full simulations, especially in the non-linear regime of structure growth. To address this problem we develop a matrix generalization of Convergence Acceleration by Regression and Pooling (CARPool) to combine a small number of simulations with fast surrogates and obtain low-noise estimates of $\boldsymbolΣ$ that are unbiased by construction. Our numerical examples use CARPool to combine GADGET-III $N$-body simulations with fast surrogates computed using COmoving Lagrangian Acceleration (COLA). Even at the challenging redshift $z=0.5$, we find variance reductions of at least $\mathcal{O}(10^1)$ and up to $\mathcal{O}(10^4)$ for the elements of the matter power spectrum covariance matrix on scales $8.9\times 10^{-3}<k_\mathrm{max} <1.0$ $h {\rm Mpc^{-1}}$. We demonstrate comparable performance for the covariance of the matter bispectrum, the matter correlation function and probability density function of the matter density field. We compare eigenvalues, likelihoods, and Fisher matrices computed using the CARPool covariance estimate with the standard sample covariance estimators and generally find considerable improvement except in cases where $Σ$ is severely ill-conditioned.

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arXiv2022

Bayesian Control Variates for optimal covariance estimation with pairs of simulations and surrogates

Predictions of the mean and covariance matrix of summary statistics are critical for confronting cosmological theories with observations, not least for likelihood approximations and parameter inference. The price to pay for accurate estimates is the extreme cost of running $N$-body and hydrodynamics simulations. Approximate solvers, or surrogates, greatly reduce the computational cost but can introduce significant biases, for example in the non-linear regime of cosmic structure growth. We propose "CARPool Bayes", an approach to solve the inference problem for both the means and covariances using a combination of simulations and surrogates. Our framework allows incorporating prior information for the mean and covariance. We derive closed-form solutions for Maximum A Posteriori covariance estimates that are efficient Bayesian shrinkage estimators, guarantee positive semi-definiteness, and can optionally leverage analytical covariance approximations. We discuss choices of the prior and propose a simple procedure for obtaining optimal prior hyperparameter values with a small set of test simulations. We test our method by estimating the covariances of clustering statistics of GADGET-III $N$-body simulations at redshift $z=0.5$ using surrogates from a 100-1000$\times$ faster particle-mesh code. Taking the sample covariance from 15,000 simulations as the truth, and using an empirical Bayes prior with diagonal blocks, our estimator produces nearly identical Fisher matrix contours for $Λ$CDM parameters using only $15$ simulations of the non-linear dark matter power spectrum. In this case the number of simulations is so small that the sample covariance would be degenerate. We show cases where even with a naïve prior our method still improves the estimate. Our framework is applicable to a wide range of cosmological and astrophysical problems where fast surrogates are available.

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arXiv2022

Snowmass2021 CMB-HD White Paper

CMB-HD is a proposed millimeter-wave survey over half the sky that would be ultra-deep (0.5 uK-arcmin) and have unprecedented resolution (15 arcseconds at 150 GHz). Such a survey would answer many outstanding questions about the fundamental physics of the Universe. Major advances would be 1.) the use of gravitational lensing of the primordial microwave background to map the distribution of matter on small scales (k~10 h Mpc^(-1)), which probes dark matter particle properties. It will also allow 2.) measurements of the thermal and kinetic Sunyaev-Zel'dovich effects on small scales to map the gas density and velocity, another probe of cosmic structure. In addition, CMB-HD would allow us to cross critical thresholds: 3.) ruling out or detecting any new, light (< 0.1 eV) particles that were in thermal equilibrium with known particles in the early Universe, 4.) testing a wide class of multi-field models that could explain an epoch of inflation in the early Universe, and 5.) ruling out or detecting inflationary magnetic fields. CMB-HD would also provide world-leading constraints on 6.) axion-like particles, 7.) cosmic birefringence, 8.) the sum of the neutrino masses, and 9.) the dark energy equation of state. The CMB-HD survey would be delivered in 7.5 years of observing 20,000 square degrees of sky, using two new 30-meter-class off-axis crossed Dragone telescopes to be located at Cerro Toco in the Atacama Desert. Each telescope would field 800,000 detectors (200,000 pixels), for a total of 1.6 million detectors.

astro-ph.CO↗

arXiv2022

Breaking baryon-cosmology degeneracy with the electron density power spectrum

Uncertain feedback processes in galaxies affect the distribution of matter, currently limiting the power of weak lensing surveys. If we can identify cosmological statistics that are robust against these uncertainties, or constrain these effects by other means, then we can enhance the power of current and upcoming observations from weak lensing surveys such as DES, Euclid, the Rubin Observatory, and the Roman Space Telescope. In this work, we investigate the potential of the electron density auto-power spectrum as a robust probe of cosmology and baryonic feedback. We use a suite of (magneto-)hydrodynamic simulations from the CAMELS project and perform an idealized analysis to forecast statistical uncertainties on a limited set of cosmological and physically-motivated astrophysical parameters. We find that the electron number density auto-correlation, measurable through either kinematic Sunyaev-Zel'dovich observations or through Fast Radio Burst dispersion measures, provides tight constraints on $Ω_{m}$ and the mean baryon fraction in intermediate-mass halos, $\bar{f}_{\mathrm{bar}}$. By obtaining an empirical measure for the associated systematic uncertainties, we find these constraints to be largely robust to differences in baryonic feedback models implemented in hydrodynamic simulations. We further discuss the main caveats associated with our analysis, and point out possible directions for future work.

astro-ph.CO↗

arXiv2021

Simulation-Based Inference of Reionization Parameters From 3D Tomographic 21 cm Lightcone Images

Tomographic three-dimensional 21 cm images from the epoch of reionization contain a wealth of information about the reionization of the intergalactic medium by astrophysical sources. Conventional power spectrum analysis cannot exploit the full information in the 21 cm data because the 21 cm signal is highly non-Gaussian due to reionization patchiness. We perform a Bayesian inference of the reionization parameters where the likelihood is implicitly defined through forward simulations using density estimation likelihood-free inference (DELFI). We adopt a trained 3D Convolutional Neural Network (CNN) to compress the 3D image data into informative summaries (DELFI-3D CNN). We show that this method recovers accurate posterior distributions for the reionization parameters. Our approach outperforms earlier analysis based on two-dimensional 21 cm images. In contrast, an MCMC analysis of the 3D lightcone-based 21 cm power spectrum alone and using a standard explicit likelihood approximation results in less accurate credible parameter regions than inferred by the DELFI-3D CNN, both in terms of the location and shape of the contours. Our proof-of-concept study implies that the DELFI-3D CNN can effectively exploit more information in the 3D 21 cm images than a 2D CNN or power spectrum analysis. This technique can be readily extended to include realistic effects and is therefore a promising approach for the scientific interpretation of future 21 cm observation data.

astro-ph.CO↗

arXiv2021

Bayesian estimation of our local motion from the Planck-2018 CMB temperature map

The largest fluctuation in the CMB sky is the CMB dipole, which is believed to be caused by the motion of our observation frame with respect to the CMB rest frame. This motion accounts for the known motion of the Solar System barycentre with a best-fit amplitude of $369$ km/s, in the direction ($\ell= 264^\circ$, $b=48^\circ$) in galactic coordinates. Along with the CMB dipole signal, this motion also causes an inevitable signature of statistical anisotropy in the higher multipoles due to the modulation and aberration of the CMB temperature and polarization fields. This leads to a correlation between adjacent CMB multipoles causing a non-zero value of the off-diagonal terms in the covariance matrix which can be captured in terms of the dipolar spectra of the bipolar spherical harmonics (BipoSH). In our work, we jointly infer the CMB power spectrum and the BipoSH spectrum in a Bayesian framework using the $\textit{Planck}$-2018 $\texttt{SMICA}$ temperature map. We detect amplitude and direction of the local motion consistent with the canonical value $v=369$ km/s inferred from CMB dipole with a statistical significance of $4.54σ$, $4.97σ$ and $5.23σ$ respectively from the masked temperature map with the available sky fraction $40.1\%$, $59.1\%$, and $72.2\%$, confirming the common origin of both the signals. The Bayes factor in favor of the canonical value is between $7$ to $8$ depending on the choice of mask. But it strongly disagrees (by a value of the Bayes factor about $10^{-10}-10^{-11}$) with a higher value of local motion which one can infer from the amplitude of the dipole signal obtained from the CatWISE2020 quasar catalog using the WISE and NEOWISE data set.

astro-ph.CO↗

arXiv2021

Single frequency CMB B-mode inference with realistic foregrounds from a single training image

With a single training image and using wavelet phase harmonic augmentation, we present polarized Cosmic Microwave Background (CMB) foreground marginalization in a high-dimensional likelihood-free (Bayesian) framework. We demonstrate robust foreground removal using only a single frequency of simulated data for a BICEP-like sky patch. Using Moment Networks we estimate the pixel-level posterior probability for the underlying {E,B} signal and validate the statistical model with a quantile-type test using the estimated marginal posterior moments. The Moment Networks use a hierarchy of U-Net convolutional neural networks. This work validates such an approach in the most difficult limiting case: pixel-level, noise-free, highly non-Gaussian dust foregrounds with a single training image at a single frequency. For a real CMB experiment, a small number of representative sky patches would provide the training data required for full cosmological inference. These results enable robust likelihood-free, simulation-based parameter and model inference for primordial B-mode detection using observed CMB polarization data.

astro-ph.CO↗

arXiv2021

Detecting Neutrino Mass by Combining Matter Clustering, Halos, and Voids

We quantify the information content of the non-linear matter power spectrum, the halo mass function, and the void size function, using the Quijote $N$-body simulations. We find that these three statistics exhibit very different degeneracies amongst the cosmological parameters, and thus the combination of all three probes enables the breaking of degeneracies, in turn yielding remarkably tight constraints. We perform a Fisher analysis using the full covariance matrix, including all auto- and cross-correlations, finding that this increases the information content for neutrino mass compared to a correlation-free analysis. The multiplicative improvement of the constraints on the cosmological parameters obtained by combining all three probes compared to using the power spectrum alone are: 137, 5, 8, 20, 10, and 43, for $Ω_m$, $Ω_b$, $h$, $n_s$, $σ_8$, and $M_ν$, respectively. The marginalized error on the sum of the neutrino masses is $σ(M_ν)=0.018\,{\rm eV}$ for a cosmological volume of $1\,(h^{-1}{\rm Gpc})^3$, using $k_{\max}=0.5\,h{\rm Mpc}^{-1}$, and without CMB priors. We note that this error is an underestimate insomuch as we do not consider super-sample covariance, baryonic effects, and realistic survey noises and systematics. On the other hand, it is an overestimate insomuch as our cuts and binning are suboptimal due to restrictions imposed by the simulation resolution. Given upcoming galaxy surveys will observe volumes spanning $\sim 100\,(h^{-1}{\rm Gpc})^3$, this presents a promising new avenue to measure neutrino mass without being restricted by the need for accurate knowledge of the optical depth, which is required for CMB-based measurements. Furthermore, the improved constraints on other cosmological parameters, notably $Ω_m$, may also be competitive with CMB-based measurements.

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arXiv2021

The GIGANTES dataset: precision cosmology from voids in the machine learning era

We present GIGANTES, the most extensive and realistic void catalog suite ever released -- containing over 1 billion cosmic voids covering a volume larger than the observable Universe, more than 20 TB of data, and created by running the void finder VIDE on QUIJOTE's halo simulations. The expansive and detailed GIGANTES suite, spanning thousands of cosmological models, opens up the study of voids, answering compelling questions: Do voids carry unique cosmological information? How is this information correlated with galaxy information? Leveraging the large number of voids in the GIGANTES suite, our Fisher constraints demonstrate voids contain additional information, critically tightening constraints on cosmological parameters. We use traditional void summary statistics (void size function, void density profile) and the void auto-correlation function, which independently yields an error of $0.13\,\mathrm{eV}$ on $\sum\,m_ν$ for a 1 $h^{-3}\mathrm{Gpc}^3$ simulation, without CMB priors. Combining halos and voids we forecast an error of $0.09\,\mathrm{eV}$ from the same volume. Extrapolating to next generation multi-Gpc$^3$ surveys such as DESI, Euclid, SPHEREx, and the Roman Space Telescope, we expect voids should yield an independent determination of neutrino mass. Crucially, GIGANTES is the first void catalog suite expressly built for intensive machine learning exploration. We illustrate this by training a neural network to perform likelihood-free inference on the void size function. Cosmology problems provide an impetus to develop novel deep learning techniques, leveraging the symmetries embedded throughout the universe from physical laws, interpreting models, and accurately predicting errors. With GIGANTES, machine learning gains an impressive dataset, offering unique problems that will stimulate new techniques.

astro-ph.CO↗

arXiv2021

The Age of the Universe with Globular Clusters: Reducing Systematic Uncertainties

The dominant systematic uncertainty in the age determination of galactic globular clusters is the depth of the convection envelope of the stars. This parameter is partially degenerate with metallicity which is in turn degenerate with age. However, if the metal content, distance and extinction are known, the position and morphology of the red giant branch in a color-magnitude diagram are mostly sensitive to the value of the depth of the convective envelope. Therefore, using external, precise metallicity determinations this degeneracy and thus the systematic error in age, can be reduced. Alternatively, the morphology of the red giant branch of globular clusters color magnitude diagram can also be used to achieve the same. We demonstrate that globular cluster red giant branches are well fitted by values of the depth of the convection envelope consistent with those obtained for the Sun and this finding is robust to the adopted treatment of the stellar physics. With these findings, the uncertainty in the depth of the convection envelope is no longer the dominant contribution to the systematic error in the age determination of the oldest globular clusters, reducing it from $0.5$ to $0.23$ or $0.33$ Gyr, depending on the methodology adopted: i.e., whether resorting to external data (spectroscopic metallicity determinations) or relying solely on the morphology of the clusters's color-magnitude diagrams. This results in an age of the Universe $t_{\rm U}=13.5^{+0.16}_{-0.14} {\rm (stat.)} \pm 0.23(0.33) ({\rm sys.})$ at 68\% confidence level, accounting for the formation time of globular clusters and its uncertainty. An uncertainty of 0.27(0.36) Gyr if added in quadrature. This agrees well with $13.8 \pm 0.02$ Gyr, the cosmological model-dependent value inferred by the Planck mission assuming the $Λ$CDM model.

astro-ph.GA↗

arXiv2021

Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields

We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. To do so, we explore parameter inference for two cases where the information content is calculable analytically: Gaussian random fields whose covariance depends on parameters through the power spectrum; and correlated lognormal fields with cosmological power spectra. We compare two inference techniques: i) explicit field-level inference using the known likelihood and ii) implicit likelihood inference with maximally informative summary statistics compressed via Information Maximising Neural Networks (IMNNs). We find that a) summaries obtained from convolutional neural network compression do not lose information and therefore saturate the known field information content, both for the Gaussian covariance and the lognormal cases, b) simulation-based inference using these maximally informative nonlinear summaries recovers nearly losslessly the exact posteriors of field-level inference, bypassing the need to evaluate expensive likelihoods or invert covariance matrices, and c) even for this simple example, implicit, simulation-based likelihood incurs a much smaller computational cost than inference with an explicit likelihood. This work uses a new IMNNs implementation in $\texttt{Jax}$ that can take advantage of fully-differentiable simulation and inference pipeline. We also demonstrate that a single retraining of the IMNN summaries effectively achieves the theoretically maximal information, enhancing the robustness to the choice of fiducial model where the IMNN is trained.

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