Creating synthetic universes in a computer

Baugh, C. M.

doi:10.1098/rsta.2008.0192

Cited by 13 publications

(15 citation statements)

References 37 publications

(44 reference statements)

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“…For a clustering analysis of an observed galaxy catalogue, the demands on the Monte Carlo approach are even greater as the N ‐body simulations need to be populated with galaxies, according to some prescription, with the goal of statistically reproducing the galaxy sample as faithfully as possible (see e.g. Baugh 2008 for a review of the techniques used to build synthetic galaxy catalogues).…”

Section: Review Of Error Estimation Methodsmentioning

confidence: 99%

“…This approach will inevitably involve N ‐body simulations, in order to accurately model the underlying dark matter, and so becomes computationally expensive. Various techniques have been adopted in the literature to populate such simulations with galaxies (for a survey see Baugh 2008). The number of simulations required is large, running into several tens to get reasonable estimates of the variance and hundreds or even thousands to get accurate covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

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Statistical analysis of galaxy surveys - I. Robust error estimation for two-point clustering statistics

Norberg¹,

Baugh²,

Gaztañaga³

et al. 2009

Monthly Notices of the Royal Astronomical Society

378

374

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We present a test of different error estimators for two-point clustering statistics, appropriate for present and future large galaxy redshift surveys. Using an ensemble of very large dark matter CDM N-body simulations, we compare internal error estimators (jackknife and bootstrap) to external ones (Monte Carlo realizations). For three-dimensional clustering statistics, we find that none of the internal error methods investigated is able to reproduce either accurately or robustly the errors of external estimators on 1 to 25 h −1 Mpc scales. The standard bootstrap overestimates the variance of ξ (s) by ∼40 per cent on all scales probed, but recovers, in a robust fashion, the principal eigenvectors of the underlying covariance matrix. The jackknife returns the correct variance on large scales, but significantly overestimates it on smaller scales. This scale dependence in the jackknife affects the recovered eigenvectors, which tend to disagree on small scales with the external estimates. Our results have important implications for fitting models to galaxy clustering measurements. For example, in a two-parameter fit to the projected correlation function, we find that the standard bootstrap systematically overestimates the 95 per cent confidence interval, while the jackknife method remains biased, but to a lesser extent. Ignoring the systematic bias, the scatter between realizations, for Gaussian statistics, implies that a 2σ confidence interval, as inferred from an internal estimator, corresponds in practice to anything from 1σ to 3σ . By oversampling the subvolumes, we find that it is possible, at least for the cases we consider, to obtain robust bootstrap variances and confidence intervals that agree with external error estimates. Our results are applicable to two-point statistics, like ξ (s) and w p (r p ), measured in large redshift surveys, and show that the interpretation of clustering measurements with internally estimated errors should be treated with caution.

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Section: Review Of Error Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical analysis of galaxy surveys - I. Robust error estimation for two-point clustering statistics

Norberg¹,

Baugh²,

Gaztañaga³

et al. 2009

Monthly Notices of the Royal Astronomical Society

378

374

View full text Add to dashboard Cite

show abstract

“…As a result, the RS method must be tested and calibrated. At low redshift this can be done with spectroscopic data sets, but at higher redshifts, where the spectroscopic data are sparse, one must turn to using synthetic 'mock' catalogues based upon the latest galaxy formation models (Baugh 2008).…”

Section: Introductionmentioning

confidence: 99%

The abundance and colours of galaxies in high-redshift clusters in the cold dark matter cosmology

Merson¹,

Baugh²,

González-Pérez³

et al. 2015

Mon. Not. R. Astron. Soc.

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High redshift galaxy clusters allow us to examine galaxy formation in extreme environments.Here we compile data for 15 z > 1 galaxy clusters to test the predictions from a state-of-theart semi-analytical model of galaxy formation. The model gives a good match to the slope and zero-point of the cluster red sequence. The model is able to match the cluster galaxy luminosity function at faint and bright magnitudes, but under-estimates the number of galaxies around the break in the cluster luminosity function. We find that simply assuming a weaker dust attenuation improves the model predictions for the cluster galaxy luminosity function, but worsens the predictions for the red sequence at bright magnitudes. Examination of the properties of the bright cluster galaxies suggests that the default dust attenuation is large due to these galaxies having large reservoirs of cold gas as well as small radii. We find that matching the luminosity function and colours of high redshift cluster galaxies, whilst remaining consistent with local observations, poses a challenge for galaxy formation models.

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“…The principal way to study errors on clustering measurements from galaxy surveys is through an accurate model of the experiment itself (Baugh 2008). For the case of relevance here (the spatial distribution of galaxies), this is optimally achieved in a three step process.…”

Section: Introductionmentioning

confidence: 99%

Extending the halo mass resolution of N-body simulations

Angulo

Baugh

Frenk

et al. 2014

Monthly Notices of the Royal Astronomical Society

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We present a scheme to extend the halo mass resolution of N -body simulations of the hierarchical clustering of dark matter. The method uses the density field of the simulation to predict the number of sub-resolution dark matter haloes expected in different regions. The technique requires as input the abundance of haloes of a given mass and their average clustering, as expressed through the linear and higher order bias factors. These quantities can be computed analytically or, more accurately, derived from a higher resolution simulation as done here. Our method can recover the abundance and clustering in real-and redshift-space of haloes with mass below ∼ 7.5 × 10 13 h −1 M ⊙ at z = 0 to better than 10%. We demonstrate the technique by applying it to an ensemble of 50 low resolution, largevolume N -body simulations to compute the correlation function and covariance matrix of luminous red galaxies (LRGs). The limited resolution of the original simulations results in them resolving just two thirds of the LRG population. We extend the resolution of the simulations by a factor of 30 in halo mass in order to recover all LRGs. With existing simulations it is possible to generate a halo catalogue equivalent to that which would be obtained from a N -body simulation using more than 20 trillion particles; a direct simulation of this size is likely to remain unachievable for many years. Using our method it is now feasible to build the large numbers of high-resolution large volume mock galaxy catalogues required to compute the covariance matrices necessary to analyse upcoming galaxy surveys designed to probe dark energy.

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Creating synthetic universes in a computer

Cited by 13 publications

References 37 publications

Statistical analysis of galaxy surveys - I. Robust error estimation for two-point clustering statistics

Statistical analysis of galaxy surveys - I. Robust error estimation for two-point clustering statistics

The abundance and colours of galaxies in high-redshift clusters in the cold dark matter cosmology

Extending the halo mass resolution of N-body simulations

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