nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure

Hand, Nick; Feng, Yu; Beutler, Florian; Yin, Li; Modi, Chirag; Seljak, Uroš; Slepian, Zachary

doi:10.3847/1538-3881/aadae0

Cited by 294 publications

(227 citation statements)

References 104 publications

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“…We estimate the redshift-space power spectrum multipoles with the endpoint Yamamoto estimator [51] in the third redshift bin. We employ the FFT-accelerated algorithm [52] enabled by multipole decomposition [70], implemented in the large-scale structure toolkit nbodykit [71]. We use the standard Landy-Szalay [61,63] estimator to estimate the correlation functions in the first redshift bin.…”

Section: Simulationsmentioning

confidence: 99%

Disconnected covariance of 2-point functions in large-scale structure

Singh

et al. 2019

J. Cosmol. Astropart. Phys.

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Optimal analyses using the 2-point functions of large-scale structure probes require accurate covariance matrices. A covariance matrix of the 2-point function comprises the disconnected part and the connected part. While the connected covariance only becomes important on small scales, the disconnected covariance is dominant on large scales, where the survey window has a significant impact. In this work, we develop an analytical method to compute the disconnected covariance, accounting for the window effect. Derived under the flat-sky approximation, our formalism is applicable to wide surveys by swapping in the curved-sky window functions. Our method works for both the power spectrum and the correlation function, and applies to the covariances of various probes including the multipoles and the wedges of 3D clustering, the angular and the projected statistics of clustering and shear, as well as the cross covariances between different probes. We verify the analytic covariance against the sample covariance from the galaxy mock simulations in two test cases: (1) the power spectrum multipole covariance, and (2) the joint covariance of the projected correlation function and the correlation function multipoles. Our method achieve good agreement with the mocks, while at a negligible computational cost. Unlike mocks, our analytic covariance is free of sampling noise, which often leads to numerical problems and the need to inflate the errors. In addition, our method can use the best-fit power spectrum as input, in contrast to the standard procedure of using a fiducial model that may deviate significantly from the truth. We also show that a naive diagonal power spectrum covariance underestimates the signal-to-noise ratio compared to our analytic covariance. The code that accompanies this paper is available at https://github.com/eelregit/covdisc. arXiv:1811.05714v2 [astro-ph.CO]

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Section: Simulationsmentioning

confidence: 99%

Disconnected covariance of 2-point functions in large-scale structure

Singh

et al. 2019

J. Cosmol. Astropart. Phys.

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“…We also acknowledge support provided by Compute Ontario (www.computeontario.ca) and Compute Canada (www.computecanada.ca). We acknowledge the use of nbodykit [52], Pylians 3 , IPython [53], Matplotlib [54] and NumPy/SciPy [55].…”

Section: Acknowledgmentsmentioning

confidence: 99%

Anisotropic halo assembly bias and redshift-space distortions

Obuljen

Dalal

Percival

2019

J. Cosmol. Astropart. Phys.

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We study the effect of large-scale tidal fields on internal halo properties using a set of N-body simulations. We measure significant cross-correlations between large-scale tidal fields and several non-scalar halo properties: shapes, velocity dispersion, and angular momentum. Selection effects that couple to these non-scalar halo properties can produce anisotropic clustering even in real-space. We investigate the size of this effect and show that it can produce a non-zero quadrupole similar in size to the one generated by linear redshift-space distortions (RSD). Finally, we investigate the clustering properties of halos identified in redshift-space and find enormous deviations from the standard linear RSD model, again caused by anisotropic assembly bias. These effects could contaminate the values of cosmological parameters inferred from the observed redshift-space clustering of galaxies, groups, or 21cm emission from atomic hydrogen, if their selection depends on properties affected by halo assembly bias. We briefly discuss ways in which this effect can be measured in existing and future large-scale structure surveys.

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“…This approximation follows from the form of the correlation function of the relative displacement field which can be written as ACKNOWLEDGEMENTS We would like to thank Mikhail Ivanov and Zvonimir Vlah for useful discussions, and Vincent Desjacques, Fabian Schmidt, and Uros Seljak for comments on an earlier version of the manuscript. The simulations and analyses used the public MP-Gadget TreePM code [80, 81] developed by Yu Feng, and the public nbodykit toolkit [82, 83] developed by Nick Hand and Yu Feng. They were generated with the Hyperion cluster at the Institute for Advanced Study.…”

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confidence: 99%