The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: measurement of the growth rate of structure from the small-scale clustering of the luminous red galaxy sample

Chapman, Michael; Mohammad, Faizan G.; Zhai, Zhongxu; Percival, Will J.; Tinker, Jeremy L.; Bautista, Julian; Brownstein, Joel R.; Burtin, E.; Dawson, Kyle; Gil-Marín, Héctor; Macorra, Axel de la; Ross, Ashley J.; Rossi, Graziano; Schneider, Donald P.; Zhao, Gong-Bo

doi:10.48550/arxiv.2106.14961

Cited by 8 publications

(25 citation statements)

References 64 publications

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“…The positions of the BOSS galaxies are converted from RA, DEC and redshift to Cartesian coordinates assuming a Planck 2016 ΛCDM model with Ω m = 0.307. The choice of cosmology for this transformation has a negligible impact on the final result (Chapman et al 2021;Lange et al 2021).…”

Section: Measurement From Bossmentioning

confidence: 99%

“…Based on the Aemulus suite, Lange et al (2021) measured the linear growth rate for the BOSS-LOWZ galaxies using clustering measurement on small scales, while Chapman et al (2021) applied the emulator method of Zhai et al (2019) and performed a cosmological analysis with eBOSS LRG data using a similar range of scales. This work extends the previous works in three different ways.…”

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confidence: 99%

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The Aemulus Project V: Cosmological constraint from small-scale clustering of BOSS galaxies

Zhai¹,

Tinker²,

Banerjee³

et al. 2022

Preprint

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We analyze clustering measurements of BOSS galaxies using a simulation-based emulator of two-point statistics. We focus on the monopole and quadrupole of the redshift-space correlation function, and the projected correlation function, at scales of 0.1 ∼ 60 h −1 Mpc. Although our simulations are based on wCDM with general relativity (GR), we include a scaling parameter of the halo velocity field, γ f , defined as the amplitude of the halo velocity field relative to the GR prediction. We divide the BOSS data into three redshift bins. After marginalizing over other cosmological parameters, galaxy bias parameters, and the velocity scaling parameter, we find f σ 8 (z = 0.25) = 0.404 ± 0.03, f σ 8 (z = 0.4) = 0.444 ± 0.025 and f σ 8 (z = 0.55) = 0.385 ± 0.019. Compared with Planck observations using a flat ΛCDM model, our results are lower by 2.29σ, 1.3σ and 4.58σ respectively. These results are consistent with other recent simulation-based results at non-linear scales, including weak lensing measurements of BOSS LOWZ galaxies, two-point clustering of eBOSS LRGs, and an independent clustering analysis of BOSS LOWZ. All these results are generally consistent with a combination of γ 1/2 f σ 8 ≈ 0.75. We note, however, that the BOSS data is well fit assuming GR, i.e. γ f = 1. We cannot rule out an unknown systematic error in the galaxy bias model at non-linear scales, but near-future data and modeling will enhance our understanding of the galaxy-halo connection, and provide a strong test of new physics beyond the standard model.

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Section: Measurement From Bossmentioning

confidence: 99%

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confidence: 99%

The Aemulus Project V: Cosmological constraint from small-scale clustering of BOSS galaxies

Zhai¹,

Tinker²,

Banerjee³

et al. 2022

Preprint

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“…Comparable gains in cosmological constraining power from the nonlinear regime of BOSS LOWZ galaxies were also found in Wibking et al (2020). In closely related work analyzing the clustering of the Luminous Red Galaxy sample in eBOSS (Chapman et al 2021), it was found that constraints on f σ 8 are improved by 70% when including information from nonlinear scales.…”

Section: Introductionmentioning

confidence: 75%

Differentiable Predictions for Large Scale Structure with SHAMNet

Hearin,

Ramachandra,

Becker

et al. 2021

Preprint

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In simulation-based models of the galaxy-halo connection, theoretical predictions for galaxy clustering and lensing are typically made based on Monte Carlo realizations of a mock universe. In this paper, we use Subhalo Abundance Matching (SHAM) as a toy model to introduce an alternative to stochastic predictions based on mock population, demonstrating how to make simulation-based predictions for clustering and lensing that are both exact and differentiable with respect to the parameters of the model. Conventional implementations of SHAM are based on iterative algorithms such as Richardson-Lucy deconvolution; here we use the JAX library for automatic differentiation to train SHAMNet, a neural network that accurately approximates the stellar-to-halo mass relation (SMHM) defined by abundance matching. In our approach to making differentiable predictions of large scale structure, we map parameterized PDFs onto each simulated halo, and calculate gradients of summary statistics of the galaxy distribution by using autodiff to propagate the gradients of the SMHM through the statistical estimators used to measure one-and two-point functions. Our techniques are quite general, and we conclude with an overview of how they can be applied in tandem with more complex, higher-dimensional models, creating the capability to make differentiable predictions for the multi-wavelength universe of galaxies.

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“…40 of these simulations form a training set that has already been used to successfully train an emulator for the correlation function in redshift space (Zhai et al 2019). This emulator has recently been used to measure the growth rate from the eBOSS LRG sample (Chapman et al 2021). These 40 simulations sample a seven dimensional cosmological parameter space, with parameters {Ω 𝑚 , Ω 𝑏 , 𝜎 8 , ℎ, 𝑛 𝑠 , 𝑁 eff , 𝑤 0 }.…”

Section: Parameter Space and Training Datamentioning

confidence: 99%

$\texttt{matryoshka}$: Halo Model Emulator for the Galaxy Power Spectrum

Donald-McCann,

Beutler,

Koyama

et al. 2021

Preprint

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We present matryoshka, a suite of neural network based emulators and accompanying Python package that have been developed with the goal of producing fast and accurate predictions of the nonlinear galaxy power spectrum. The suite of emulators consists of four linear component emulators, from which fast linear predictions of the power spectrum can be made, allowing all nonlinearities to be included in predictions from a nonlinear boost component emulator. The linear component emulators includes an emulator for the matter transfer function that produces predictions in ∼ 0.0004 s, with an error of < 0.08% (at 1𝜎 level) on scales 10 −4 ℎ Mpc −1 < 𝑘 < 10 1 ℎ Mpc −1 . In this paper we demonstrate matryoshka by training the nonlinear boost component emulator with analytic training data calculated with HALOFIT, that has been designed to replicate training data that would be generated using numerical simulations. Combining all the component emulator predictions we achieve an accuracy of < 0.75% (at 1𝜎 level) when predicting the real space nonlinear galaxy power spectrum on scales 0.0025 ℎ Mpc −1 < 𝑘 < 1 ℎ Mpc −1 . We use matryoshka to investigate the impact of the analysis setup on cosmological constraints by conducting several full shape analyses of the real space galaxy power spectrum. Specifically we investigate the impact of the minimum scale (or 𝑘 max ), finding an improvement of ∼ 1.8× in the constraint on 𝜎 8 by pushing 𝑘 max from 𝑘 max = 0.25 ℎ Mpc −1 to 𝑘 max = 0.85 ℎ Mpc −1 , highlighting the potential gains when using clustering emulators such as matryoshka in cosmological analyses. matryoshka is publicly available at https://github.com/JDonaldM/Matryoshka.

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The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: measurement of the growth rate of structure from the small-scale clustering of the luminous red galaxy sample

Cited by 8 publications

References 64 publications

The Aemulus Project V: Cosmological constraint from small-scale clustering of BOSS galaxies

The Aemulus Project V: Cosmological constraint from small-scale clustering of BOSS galaxies

Differentiable Predictions for Large Scale Structure with SHAMNet

$\texttt{matryoshka}$: Halo Model Emulator for the Galaxy Power Spectrum

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