arXiv subjects

George Stein

Publications and source records attributed to George Stein.

27 records · Page 2Linked to original sources

arXiv2019

Comparing approximate methods for mock catalogues and covariance matrices I: correlation function

This paper is the first in a set that analyses the covariance matrices of clustering statistics obtained from several approximate methods for gravitational structure formation. We focus here on the covariance matrices of anisotropic two-point correlation function measurements. Our comparison includes seven approximate methods, which can be divided into three categories: predictive methods that follow the evolution of the linear density field deterministically (ICE-COLA, Peak Patch, and Pinocchio), methods that require a calibration with N-body simulations (Patchy and Halogen), and simpler recipes based on assumptions regarding the shape of the probability distribution function (PDF) of density fluctuations (log-normal and Gaussian density fields). We analyse the impact of using covariance estimates obtained from these approximate methods on cosmological analyses of galaxy clustering measurements, using as a reference the covariances inferred from a set of full N-body simulations. We find that all approximate methods can accurately recover the mean parameter values inferred using the N-body covariances. The obtained parameter uncertainties typically agree with the corresponding N-body results within 5% for our lower mass threshold, and 10% for our higher mass threshold. Furthermore, we find that the constraints for some methods can differ by up to 20% depending on whether the halo samples used to define the covariance matrices are defined by matching the mass, number density, or clustering amplitude of the parent N-body samples. The results of our configuration-space analysis indicate that most approximate methods provide similar results, with no single method clearly outperforming the others.

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arXiv2019

Joint power spectrum and voxel intensity distribution forecast on the CO luminosity function with COMAP

We develop a framework for joint constraints on the CO luminosity function based on power spectra (PS) and voxel intensity distributions (VID), and apply this to simulations of COMAP, a CO intensity mapping experiment. This Bayesian framework is based on a Markov chain Monte Carlo (MCMC) sampler coupled to a Gaussian likelihood with a joint PS + VID covariance matrix computed from a large number of fiducial simulations, and re-calibrated with a small number of simulations per MCMC step. The simulations are based on dark matter halos from fast peak patch simulations combined with the $L_\text{CO}(M_\text{halo})$ model of Li et al. (2016). We find that the relative power to constrain the CO luminosity function depends on the luminosity range of interest. In particular, the VID is more sensitive at both small and large luminosities, while the PS is more sensitive at intermediate luminosities. The joint analysis is superior to using either observable separately. When averaging over CO luminosities ranging between $L_\text{CO} = 10^4-10^7L_\odot$, and over 10 cosmological realizations of COMAP Phase 2, the uncertainties (in dex) are larger by 58 % and 30 % for the PS and VID, respectively, when compared to the joint analysis (PS + VID). This method is generally applicable to any other random field, with a complicated likelihood, as long a fast simulation procedure is available.

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arXiv2019

Comparing approximate methods for mock catalogues and covariance matrices II: Power spectrum multipoles

We study the accuracy of several approximate methods for gravitational dynamics in terms of halo power spectrum multipoles and their estimated covariance matrix. We propagate the differences in covariances into parameter constrains related to growth rate of structure, Alcock-Paczynski distortions and biasing. We consider seven methods in three broad categories: algorithms that solve for halo density evolution deterministically using Lagrangian trajectories (ICE-COLA, Pinocchio and PeakPatch), methods that rely on halo assignment schemes onto dark-matter overdensities calibrated with a target N-body run (Halogen, Patchy) and two standard assumptions about the full density PDF (Gaussian and Lognormal). We benchmark their performance against a set of three hundred N-body simulations, running similar sets of approximate simulations with matched initial conditions, for each method. We find that most methods reproduce the monopole to within $5\%$, while residuals for the quadrupole are sometimes larger and scale dependent. The variance of the multipoles is typically reproduced within $10\%$. Overall, we find that covariances built from approximate simulations yield errors on model parameters within $10\%$ of those from the N-body based covariance.

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arXiv2018

A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues

For modern large-scale structure survey techniques it has become standard practice to test data analysis pipelines on large suites of mock simulations, a task which is currently prohibitively expensive for full N-body simulations. Instead of calculating this costly gravitational evolution, we have trained a three-dimensional deep Convolutional Neural Network (CNN) to identify dark matter protohalos directly from the cosmological initial conditions. Training on halo catalogues from the Peak Patch semi-analytic code, we test various CNN architectures and find they generically achieve a Dice coefficient of ~92% in only 24 hours of training. We present a simple and fast geometric halo finding algorithm to extract halos from this powerful pixel-wise binary classifier and find that the predicted catalogues match the mass function and power spectra of the ground truth simulations to within ~10%. We investigate the effect of long-range tidal forces on an object-by-object basis and find that the network's predictions are consistent with the non-linear ellipsoidal collapse equations used explicitly by the Peak Patch algorithm.

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arXiv2018

The mass-Peak Patch algorithm for fast generation of deep all-sky dark matter halo catalogues and its N-Body validation

We present a detailed description and validation of our massively-parallel update to the mass-Peak Patch method, a fully predictive initial-space algorithm to quickly generate dark matter halo catalogues in very large cosmological volumes. We perform an extensive systematic comparison to a suite of N-body simulations covering a broad range of redshifts and simulation resolutions, and find that, without any parameter fitting, our method is able to generally reproduce N-body results while typically using over 3 orders of magnitude less CPU time, and a fraction of the memory cost. Instead of calculating the full non-linear gravitational collapse determined by an N-body simulation, the mass-Peak Patch method finds an overcomplete set of just-collapsed structures around a hierarchy of density-peak points by coarse-grained (homogeneous) ellipsoidal dynamics. A complete set of mass-peaks, or halos, is then determined by exclusion of overlapping patches, and second order Lagrangian displacements are used to move the halos to their final positions and to give their flow velocities. Our results show that the mass-Peak Patch method is well-suited for creating large ensembles of halo catalogues to mock cosmological surveys, and to aid in complex statistical interpretations of cosmological models.

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arXiv2018

Comparing approximate methods for mock catalogues and covariance matrices III: Bispectrum

We compare the measurements of the bispectrum and the estimate of its covariance obtained from a set of different methods for the efficient generation of approximate dark matter halo catalogs to the same quantities obtained from full N-body simulations. To this purpose we employ a large set of three-hundred realisations of the same cosmology for each method, run with matching initial conditions in order to reduce the contribution of cosmic variance to the comparison. In addition, we compare how the error on cosmological parameters such as linear and nonlinear bias parameters depends on the approximate method used for the determination of the bispectrum variance. As general result, most methods provide errors within 10% of the errors estimated from N-body simulations. Exceptions are those methods requiring calibration of the clustering amplitude but restrict this to two-point statistics. Finally we test how our results are affected by being limited to a few hundreds measurements from N-body simulation, and therefore to the bispectrum variance, by comparing with a larger set of several thousands realisations performed with one approximate method.

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arXiv2018

Weak-Lensing Mass Calibration of ACTPol Sunyaev-Zel'dovich Clusters with the Hyper Suprime-Cam Survey

We present weak-lensing measurements using the first-year data from the Hyper Suprime-Cam Strategic Survey Program on the Subaru telescope for eight galaxy clusters selected through their thermal Sunyaev-Zel'dovich (SZ) signal measured at 148 GHz with the Atacama Cosmology Telescope Polarimeter experiment. The overlap between the two surveys in this work is 33.8 square degrees, before masking bright stars. The signal-to-noise ratio of individual cluster lensing measurements ranges from 2.2 to 8.7, with a total of 11.1 for the stacked cluster weak-lensing signal. We fit for an average weak-lensing mass distribution using three different profiles, a Navarro-Frenk-White profile, a dark-matter-only emulated profile, and a full cosmological hydrodynamic emulated profile. We interpret the differences among the masses inferred by these models as a systematic error of 10\%, which is currently smaller than the statistical error. We obtain the ratio of the SZ-estimated mass to the lensing-estimated mass (the so-called hydrostatic mass bias $1-b$) of $0.74^{+0.13}_{-0.12}$, which is comparable to previous SZ-selected clusters from the Atacama Cosmology Telescope and from the {\sl Planck} Satellite. We conclude with a discussion of the implications for cosmological parameters inferred from cluster abundances compared to cosmic microwave background primary anisotropy measurements.

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arXiv2017

Line-Intensity Mapping: 2017 Status Report

Following the first two annual intensity mapping workshops at Stanford in March 2016 and Johns Hopkins in June 2017, we report on the recent advances in theory, instrumentation and observation that were presented in these meetings and some of the opportunities and challenges that were identified looking forward. With preliminary detections of CO, [CII], Lya and low-redshift 21cm, and a host of experiments set to go online in the next few years, the field is rapidly progressing on all fronts, with great anticipation for a flood of new exciting results. This current snapshot provides an efficient reference for experts in related fields and a useful resource for nonspecialists. We begin by introducing the concept of line-intensity mapping and then discuss the broad array of science goals that will be enabled, ranging from the history of star formation, reionization and galaxy evolution to measuring baryon acoustic oscillations at high redshift and constraining theories of dark matter, modified gravity and dark energy. After reviewing the first detections reported to date, we survey the experimental landscape, presenting the parameters and capabilities of relevant instruments such as COMAP, mmIMe, AIM-CO, CCAT-p, TIME, CONCERTO, CHIME, HIRAX, HERA, STARFIRE, MeerKAT/SKA and SPHEREx. Finally, we describe recent theoretical advances: different approaches to modeling line luminosity functions, several techniques to separate the desired signal from foregrounds, statistical methods to analyze the data, and frameworks to generate realistic intensity map simulations.

astro-ph.CO↗

arXiv2014

Testing Inflation with Large Scale Structure: Connecting Hopes with Reality

The statistics of primordial curvature fluctuations are our window into the period of inflation, where these fluctuations were generated. To date, the cosmic microwave background has been the dominant source of information about these perturbations. Large scale structure is however from where drastic improvements should originate. In this paper, we explain the theoretical motivations for pursuing such measurements and the challenges that lie ahead. In particular, we discuss and identify theoretical targets regarding the measurement of primordial non-Gaussianity. We argue that when quantified in terms of the local (equilateral) template amplitude $f_{\rm NL}^{\rm loc}$ ($f_{\rm NL}^{\rm eq}$), natural target levels of sensitivity are $Δf_{\rm NL}^{\rm loc, eq.} \simeq 1$. We highlight that such levels are within reach of future surveys by measuring 2-, 3- and 4-point statistics of the galaxy spatial distribution. This paper summarizes a workshop held at CITA (University of Toronto) on October 23-24, 2014.

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