arXiv subjects

Hume A. Feldman

Publications and source records attributed to Hume A. Feldman.

At least 19 recordsLinked to original sources

arXiv2026

A low value of $H_0$ in tension with the distance ladder from Megamasers using peculiar velocity reconstruction

Megamaser distances can be measured without using the traditional distance ladder. As such, they provide an independent probe of the local expansion rate, quantified by the Hubble constant $H_0$. We re-analyze megamaser distance and redshift data to obtain a new, more accurate, estimate of $H_0$. The main improvement in our analysis is the use of a new velocity field reconstruction to correct redshifts for peculiar velocities. This reconstruction utilizes constrained cosmological simulations in addition to redshift survey data to more accurately capture both coherent flows as well as nonGaussian individual galaxy motions. We argue that previous estimates of $H_0$ from megamaser data were susceptible to bias due to peculiar velocities. By correcting for peculiar motions using this new reconstruction we obtain a value of $H_0$ that is consistent with the value from the Cosmic Microwave Background (CMB) and in tension with the value from the local distance ladder at greater than a $2σ$ level, whereas previous results showed consistency with the distance ladder and tension with the CMB at greater than $2σ$. Our results suggest that the Hubble tension is due to some heretofore undetermined systematic in the distance ladder rather than new physics.

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arXiv2026

The Origins of the Bulk flow

We analyze the origin of the large-scale bulk flow using the CosmicFlows-4 (CF4) peculiar-velocity catalog. We decompose the observed motions into internal components, generated by mass fluctuations within 200 Mpc/h, and external ones arising from structures beyond this volume. A weighted-average technique is developed to test the model's self-consistency while minimizing the impact of non-Gaussian distance errors. The CF4 velocities show excellent agreement with the predicted internal field, yielding beta = 0.31 pm 0.01. We also determine that the value of the Hubble constant that should be used for calculating peculiar velocities from the CF4 to be H0 = 75.0 pm 0.1 km/s/Mpc, consistent with CF4 calibrations. Using the minimum-variance formalism, we further separate the bulk flow into its internal and external contributions and find that the observed large-scale bulk flow is dominated by sources beyond 200 Mpc/h. The amplitude of this externally driven flow increases monotonically with scale, consistent with the influence of a distant, massive overdensity. These findings reinforce the reliability of the CF4 velocity field while calling into question the assumption of a spatially uniform flow generated by external sources. Our results challenge the commonly made hypothesis that the flow in our local volume due to external mass concentrations can be modeled as being spatially uniform.

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arXiv2026

Consistency of DES and DESI distances and the Standard Cosmological Model

We test the consistency of the cosmic distance-redshift relation inferred from the cosmic microwave background (CMB), the Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation measurements, and the Dark Energy Survey (DES) Type Ia supernovae within the framework of flat $Λ$CDM. DESI recovers the CMB-constrained parameter combination $(r_d h)(Ω_m/0.3)^{0.4}$ with sub-percent precision, demonstrating excellent agreement between BAO measurements at $z \sim 1$ and the acoustic scale at recombination. Imposing the CMB constraint yields an estimate of $Ω_m$ that is slightly lower than, but only in mild tension with, the Planck value. The high-redshift DES supernova sample is well described by the standard cosmological model, whereas the current low-redshift anchor sample exhibits a systematic offset of $\sim 0.05$ mag that drives much of the apparent preference for evolving dark energy. Preliminary data from the Dark Energy Bedrock All-Sky Supernova Program (DEBASS) do not show this offset, suggesting that unresolved low-redshift systematics may account for the discrepancy. These results suggest that a single flat $Λ$CDM model accurately describes the distance-redshift relation from the local Universe to recombination, placing increasingly stringent constraints on new-physics explanations of the Hubble tension.

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arXiv2026

The peculiar velocity correlation function of the Cosmicflows-4 catalog

We present an analysis of the parallel peculiar velocity correlation function using data from the Cosmicflows-4 (CF4) survey. CF4 significantly extends the depth of the peculiar velocity measurements, mitigating the impact of observers on the cosmic variance. We examine the distribution of cosmic variance using different velocity correlation estimators. The combination of the large peculiar velocity uncertainties and the anisotropy distribution of the CF4 data across the northern and southern hemispheres results in substantial statistical uncertainties in the velocity correlation function. To address this, we test different weighing schemes in the velocity correlation function and implement a more accurate peculiar velocity estimator that reduces velocity uncertainties, consequently decreasing the statistical uncertainty. Using the CF4 group dataset, we derive a growth rate of $fσ_8=0.384^{+0.116}_{-0.194}$ and a local growth rate of $fσ_8=0.569^{+0.054}_{-0.06}$ through a Markov Chain Monte Carlo method.

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arXiv2023

Analyzing the Large-Scale Bulk Flow using CosmicFlows4: Increasing Tension with the Standard Cosmological Model

We present an estimate of the bulk flow in a volume of radii $150-200h^{-1}$Mpc using the minimum variance (MV) method with data from the CosmicFlows-4 (CF4) catalog. The addition of new data in the CF4 has resulted in an increase in the estimate of the bulk flow in a sphere of radius $150h^{-1}$Mpc relative to the CosmicFlows-3 (CF3). This bulk flow has less than a $0.03\%$ chance of occurring in the Standard Cosmological Model ($Λ$CDM) with cosmic microwave background derived parameters. Given that the CF4 is deeper than the CF3, we were able to use the CF4 to accurately estimate the bulk flow on scales of $200h^{-1}$Mpc (equivalent to 266 Mpc for Hubble constant $H_o=75$ km/s/Mpc) for the first time. This bulk flow is in even greater tension with the Standard Model, having less than $0.003\%$ probability of occurring. To estimate the bulk flow accurately, we introduce a novel method to calculate distances and velocities from distance moduli that is unbiased and accurate at all distances. Our results are completely independent of the value of $H_o$.

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arXiv2021

Improved Methods for Estimating Peculiar Velocity Correlation Functions Using Volume Weighting

We present an improved method for calculating the parallel and perpendicular velocity correlation functions directly from peculiar velocity surveys using weighted maximum-likelihood estimators. A central feature of the new method is the use of position-dependent weighting scheme that reduces the influence of nearby galaxies, which are typically overrepresented relative to the more distant galaxies in most surveys. We demonstrate that the correlation functions calculated this way are less susceptible to biases due to our particular location in the Universe, and thus are more easily comparable to linear theory and between surveys. Our results suggest that the parallel velocity correlation function is a promising cosmological probe, given that it provides a better approximation of a Gaussian distribution than other velocity correlation functions and that its bias is more easily minimized by weighting. Though the position weighted parallel velocity correlation function increases the statistical uncertainty, it decreases the cosmic variance and is expected to provide more stable and tighter cosmological parameter constraints than other correlation methods in conjunction with more precise velocity surveys in the future.

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arXiv2020

Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks

The Sunyaev-Zel'dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of peculiar velocities of galaxy clusters using deep learning frameworks trained on numerical simulations to avoid the estimation of the optical depth. The image of distorted photon backgrounds are generated for idealized observations using one of the largest cosmological hydrodynamical simulations, the Magneticum simulations. The model is tested to be capable peculiar velocities from future kinetic SZ observations under different noise conditions. The deep learning algorithm displays robustness in estimating peculiar velocities from kinetic SZ effect by an improvement in accuracy of about 17% compared to the analytical approach.

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arXiv2018

Easily Interpretable Bulk Flows: Continuing Tension with the Standard Cosmological Model

We present an improved Minimal Variance (MV) method for using a radial peculiar velocity sample to estimate the average of the three-dimensional velocity field over a spherical volume, which leads to an easily interpretable bulk flow measurement. The only assumption required is that the velocity field is irrotational. The resulting bulk flow estimate is particularly insensitive to smaller scale flows. We also introduce a new constraint into the MV method that ensures that bulk flow estimates are independent of the value of the Hubble constant $H_o$; this is important given the tension between the locally measured $H_o$ and that obtained from the cosmic background radiation observations. We apply our method to the \textit{CosmicFlows-3} catalogue and find that, while the bulk flows for shallower spheres are consistent with the standard cosmological model, there is some tension between the bulk flow in a spherical volume with radius $150$\hmpc\ and its expectations; we find only a $\sim 2\%$ chance of obtaining a bulk flow as large or larger in the standard cosmological model with \textit{Planck} parameters

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arXiv2018

The Peculiar Velocity Correlation Function

We present an analysis of the two-point peculiar velocity correlation function using data from the CosmicFlows catalogues. The Millennium and MultiDark Planck 2 N-body simulations are used to estimate cosmic variance and uncertainties due to measurement errors. We compare the velocity correlation function to expectations from linear theory to constrain cosmological parameters. Using the maximum likelihood method, we find values of $Ω_m= 0.315^{+0.205}_{-0.135}$ and $σ_8=0.92^{+0.440}_{-0.295}$, consistent with the Planck and Wilkinson Microwave Anisotropy Probe CMB derived estimates. However, we find that the cosmic variance of the correlation function is large and non-Gaussian distributed, making the peculiar velocity correlation function less than ideal as a probe of large-scale structure.

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arXiv2015

Gravitational potential wells and the cosmic bulk flow

The bulk flow is a volume average of the peculiar velocities and a useful probe of the mass distribution on large scales. The gravitational instability model views the bulk flow as a potential flow that obeys a Maxwellian Distribution. We use two N-body simulations, the LasDamas Carmen and the Horizon Run, to calculate the bulk flows of various sized volumes in the simulation boxes. Once we have the bulk flow velocities as a function of scale, we investigate the mass and gravitational potential distribution around the volume. We found that matter densities can be asymmetrical and difficult to detect in real surveys, however, the gravitational potential and its gradient may provide better tools to investigate the underlying matter distribution. This study shows that bulk flows are indeed potential flows and thus provides information on the flow sources. We also show that bulk flow magnitudes follow a Maxwellian distribution on scales $>10\ h^{-1}$Mpc.

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arXiv2014

Large-scale Cosmic Flows from Cosmicflows-2 Catalog

The Cosmicflows-2 catalogue is a compendium of peculiar velocity measurements. While it has many objects in common with the COMPOSITE catalogue, a previously analysed collection of peculiar velocity data found to give an unexpectedly large bulk flow on large scales, the data in Cosmicflows-2 have been reanalysed to ensure consistency between distances measured using different methods. In particular, a focus on accurate distances led the authors of the Cosmicflows-2 to not correct for homogeneous or inhomogeneous Malmquist bias, both or which are corrected for in the COMPOSITE compilation. We find remarkable agreement between the COMPOSITE and the Cosmicflows-2 if the small EFAR sample of clusters located in two dense superclusters is removed from both surveys, giving results that are inconsistent with the $Λ$ cold dark matter standard model with Planck central parameters at the 98\% level. On smaller scales we find overall agreement between data sets and consistency with the standard model.

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arXiv2014

An Unbiased Estimator of Peculiar Velocity with Gaussian Distributed Errors for Precision Cosmology

We introduce a new estimator of the peculiar velocity of a galaxy or group of galaxies from redshift and distance estimates. This estimator results in peculiar velocity estimates which are statistically unbiased and that have errors that are Gaussian distributed, thus meeting the assumptions of analyses that rely on individual peculiar velocities. We apply this estimator to the SFI++ and the Cosmicflows-2 catalogs of galaxy distances and, using the fact that peculiar velocity estimates of distant galaxies are error dominated, examine their error distributions, The adoption of the new estimator significantly improves the accuracy and validity of studies of the large-scale peculiar velocity field and eliminates potential systematic biases, thus helping to bring peculiar velocity analysis into the era of precision cosmology. In addition, our method of examining the distribution of velocity errors should provide a useful check of the statistics of large peculiar velocity catalogs, particularly those that are compiled out of data from multiple sources.

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arXiv2013

PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks

In this paper we introduce PkANN, a freely available software package for interpolating the non-linear matter power spectrum, constructed using Artificial Neural Networks (ANNs). Previously, using Halofit to calculate matter power spectrum, we demonstrated that ANNs can make extremely quick and accurate predictions of the power spectrum. Now, using a suite of 6380 N-body simulations spanning 580 cosmologies, we train ANNs to predict the power spectrum over the cosmological parameter space spanning $3σ$ confidence level (CL) around the concordance cosmology. When presented with a set of cosmological parameters ($Ω_{\rm m} h^2, Ω_{\rm b} h^2, n_s, w, σ_8, \sum m_ν$ and redshift $z$), the trained ANN interpolates the power spectrum for $z\leq2$ at sub-per cent accuracy for modes up to $k\leq0.9\,h\textrm{Mpc}^{-1}$. PkANN is faster than computationally expensive N-body simulations, yet provides a worst-case error $<1$ per cent fit to the non-linear matter power spectrum deduced through N-body simulations. The overall precision of PkANN is set by the accuracy of our N-body simulations, at 5 per cent level for cosmological models with $\sum m_ν<0.5$ eV for all redshifts $z\leq2$. For models with $\sum m_ν>0.5$ eV, predictions are expected to be at 5 (10) per cent level for redshifts $z>1$ ($z\leq1$). The PkANN interpolator may be freely downloaded from http://zuserver2.star.ucl.ac.uk/~fba/PkANN

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arXiv2013

The Cosmic Mach Number: Comparison from Observations, Numerical Simulations and Nonlinear Predictions

We calculate the cosmic Mach number M - the ratio of the bulk flow of the velocity field on scale R to the velocity dispersion within regions of scale R. M is effectively a measure of the ratio of large-scale to small-scale power and can be a useful tool to constrain the cosmological parameter space. Using a compilation of existing peculiar velocity surveys, we calculate M and compare it to that estimated from mock catalogues extracted from the LasDamas (a LCDM cosmology) numerical simulations. We find agreement with expectations for the LasDamas cosmology at ~ 1.5 sigma CL. We also show that our Mach estimates for the mocks are not biased by selection function effects. To achieve this, we extract dense and nearly-isotropic distributions using Gaussian selection functions with the same width as the characteristic depth of the real surveys, and show that the Mach numbers estimated from the mocks are very similar to the values based on Gaussian profiles of the corresponding widths. We discuss the importance of the survey window functions in estimating their effective depths. We investigate the nonlinear matter power spectrum interpolator PkANN as an alternative to numerical simulations, in the study of Mach number.

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arXiv2012

Testing the minimum variance method for estimating large-scale velocity moments

The estimation and analysis of large-scale bulk flow moments of peculiar velocity surveys is complicated by non-spherical survey geometry, the non-uniform sampling of the matter velocity field by the survey objects and the typically large measurement errors of the measured line-of-sight velocities. Previously, we have developed an optimal `minimum variance' (MV) weighting scheme for using peculiar velocity data to estimate bulk flow moments for idealized, dense and isotropic surveys with Gaussian radial distributions, that avoids many of these complications. These moments are designed to be easy to interpret and are comparable between surveys. In this paper, we test the robustness of our MV estimators using numerical simulations. Using MV weights, we estimate the bulk flow moments for various mock catalogues extracted from the LasDamas and the Horizon Run numerical simulations and compare these estimates to the moments calculated directly from the simulation boxes. We show that the MV estimators are unbiased and negligibly affected by non-linear flows.

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arXiv2012

PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks

We investigate the interpolation of power spectra of matter fluctuations using Artificial Neural Network (PkANN). We present a new approach to confront small-scale non-linearities in the power spectrum of matter fluctuations. This ever-present and pernicious uncertainty is often the Achilles' heel in cosmological studies and must be reduced if we are to see the advent of precision cosmology in the late-time Universe. We show that an optimally trained artificial neural network (ANN), when presented with a set of cosmological parameters (Omega_m h^2, Omega_b h^2, n_s, w_0, sigma_8, m_nu and redshift z), can provide a worst-case error <=1 per cent (for z<=2) fit to the non-linear matter power spectrum deduced through N-body simulations, for modes up to k<=0.7 h/Mpc. Our power spectrum interpolator is accurate over the entire parameter space. This is a significant improvement over some of the current matter power spectrum calculators. In this paper, we detail how an accurate interpolation of the matter power spectrum is achievable with only a sparsely sampled grid of cosmological parameters. Unlike large-scale N-body simulations which are computationally expensive and/or infeasible, a well-trained ANN can be an extremely quick and reliable tool in interpreting cosmological observations and parameter estimation. This paper is the first in a series. In this method paper, we generate the non-linear matter power spectra using HaloFit and use them as mock observations to train the ANN. This work sets the foundation for Paper II, where a suite of N-body simulations will be used to compute the non-linear matter power spectra at sub-per cent accuracy, in the quasi-non-linear regime 0.1 h/Mpc <= k <= 0.9 h/Mpc. A trained ANN based on this N-body suite will be released for the scientific community.

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arXiv2012

Power Spectrum Estimation from Peculiar Velocity Catalogues

The peculiar velocities of galaxies are an inherently valuable cosmological probe, providing an unbiased estimate of the distribution of matter on scales much larger than the depth of the survey. Much research interest has been motivated by the high dipole moment of our local peculiar velocity field, which suggests a large scale excess in the matter power spectrum, and can appear to be in some tension with the LCDM model. We use a composite catalogue of 4,537 peculiar velocity measurements with a characteristic depth of 33 h-1 Mpc to estimate the matter power spectrum. We compare the constraints with this method, directly studying the full peculiar velocity catalogue, to results from Macaulay et al. (2011), studying minimum variance moments of the velocity field, as calculated by Watkins, Feldman & Hudson (2009) and Feldman, Watkins & Hudson (2010). We find good agreement with the LCDM model on scales of k > 0.01 h Mpc-1. We find an excess of power on scales of k < 0.01 h Mpc-1, although with a 1 sigma uncertainty which includes the LCDM model. We find that the uncertainty in the excess at these scales is larger than an alternative result studying only moments of the velocity field, which is due to the minimum variance weights used to calculate the moments. At small scales, we are able to clearly discriminate between linear and nonlinear clustering in simulated peculiar velocity catalogues, and find some evidence (although less clear) for linear clustering in the real peculiar velocity data.

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arXiv2012

The effect of massive neutrinos on the matter power spectrum

We investigate the impact of massive neutrinos on the distribution of matter in the semi-non-linear regime (0.1<k<0.6 h/Mpc). We present a suite of large-scale N-body simulations quantifying the scale dependent suppression of the total matter power spectrum, resulting from the free-streaming of massive neutrinos out of high-density regions. Our simulations show a power suppression of 3.5-90 per cent at k~0.6 h/Mpc for total neutrino mass, m_nu=0.05-1.9 eV respectively. We also discuss the precision levels that future cosmological datasets would have to achieve in order to distinguish the normal and inverted neutrino mass hierarchies.

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