The reconstructed power spectrum in the Zeldovich approximation

Chen, Shi-Fan; Vlah, Zvonimir; White, Martin

doi:10.1088/1475-7516/2019/09/017

Cited by 33 publications

(41 citation statements)

References 71 publications

(251 reference statements)

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“…Additional work is required to link the cosmological parameters to the observables, especially the reconstructed power spectrum 12,21 and the cross power spectrum 22 on (quasi-) nonlinear scales, and this is challenging using traditional approaches, e.g., methods based on perturbation theories.…”

Section: Extracting High-order Cosmological Information In Galaxy Sur...mentioning

confidence: 99%

Extracting high-order cosmological information in galaxy surveys with power spectra

Wang

Zhao

Koyama

et al. 2022

Preprint

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The "reconstruction" method was proposed more than a decade ago to boost the signal of baryonic acoustic oscillations measured in galaxy redshift surveys, which is one of key probes for dark energy. After moving observed galaxies in galaxy surveys back to their initial position, the reconstructed density field is closer to a linear Gaussian field, with higher-order information moved back into the power spectrum. By combining power spectra measured from the pre- and post-reconstructed galaxy samples, higher-order information beyond the 2-point power spectrum can be efficiently extracted, as power spectra can be measured more quickly than direct measurements of the bispectrum and other higher order functions. This opens a new window to easily use higher-order information when constraining cosmological models.

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Section: Extracting High-order Cosmological Information In Galaxy Sur...mentioning

confidence: 99%

Extracting high-order cosmological information in galaxy surveys with power spectra

Wang

Zhao

Koyama

et al. 2022

Preprint

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“…Where appropriate, we have assumed that reconstruction has been performed using the 'isotropic convention' Anderson et al 2014b), which attempts to remove the RSD signal during the reconstruction process by dividing the observed density field by the Kaiser factor during the computation of the displacement and without adding this factor back on to the displaced galaxies or random points. This leads to a slightly more linear BAO feature compared to other methods, at the cost of an overall reduction in clustering amplitude (Schmittfull et al 2015;White 2015;Seo et al 2016;Chen et al 2019). We also assume that a Gaussian smoothing of the density field was used and define the smoothing kernel in Fourier space as Noda et al (2019).…”

Section: Reconstructionmentioning

confidence: 99%

barry and the BAO model comparison

Hinton

Howlett

Davis

2020

Monthly Notices of the Royal Astronomical Society

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We compare the performance of four state-of-the-art models for extracting isotropic measurements of the Baryon Acoustic Oscillation (BAO) scale. To do this, we created a new, public, modular code Barry, which contains datasets, model fitting tools, and model implementations incorporating different descriptions of non-linear physics and algorithms for isolating the BAO feature. These are then evaluated for bias, correlation, and fitting strength using mock power spectra and correlation functions developed for the Sloan Digital Sky Survey Data Release 12. Our main findings are as follows: 1) All of the models can recover unbiased constraints when fit to the preand post-reconstruction simulations. 2) Models that provide physical descriptions of the damping of the BAO feature (using e.g., standard perturbation or effective-field theory arguments) report smaller errors on average, although the distribution of mock χ 2 values indicates these are underestimated. 3) Allowing the BAO damping scale to vary can provide tighter constraints for some mocks, but is an artificial improvement that only arises when noise randomly sharpens the BAO peak. 4) Unlike recent claims in the literature when utilising a BAO Extractor technique, we find no improvement in the accuracy of the recovered BAO scale. 5) We implement a procedure for combining all models into a single consensus result that improves over the standard method without obviously underestimating the uncertainties. Overall, Barry provides a framework for performing the cosmological analyses for upcoming surveys, and for rapidly testing and validating new models.

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“…Based on the Zel'dovich approximation, Eisenstein et al (2007a) proposed a standard reconstruction algorithm to improve BAO measurement accuracy. This technique has been tested with simulated data (e.g., Seo et al 2008Seo et al , 2010Mehta et al 2011;Burden et al 2014;Achitouv & Blake 2015) and been theoretically studied (e.g., Noh et al 2009;Padmanabhan et al 2009;White 2015;Seo et al 2016;Hikage et al 2017Hikage et al , 2020Chen et al 2019), and has also been extensively applied to observation data analysis (e.g., Padmanabhan et al 2012;Xu et al 2012Xu et al , 2013Anderson et al 2014;Kazin et al 2014;Ross et al 2015;Beutler et al 2016Beutler et al , 2017Hinton et al 2017). In recent years, several improved new reconstruction algorithms (e.g., the isobaric reconstruction technique, Zhu et al 2017; the iterative reconstruction technique, Schmittfull et al 2017;Hada & Eisenstein 2018; the multigrid relaxation method, Shi et al 2018; the extended fast action minimization method, Sarpa et al 2019; and the fast semidiscrete optimal transport algorithm, Lévy et al 2020) were proposed and tested not only on matter field (e.g., Pan et al 2017;Wang et al 2017) but also on more realistic halo/galaxy fields (e.g., Yu et al 2017b;Birkin et al 2019;Hada & Eisenstein 2019;Wang & Pen 2019;Sarpa et al 2021), showing that they can substantially bring back the initial information and recover the linear BAO signal.…”

Section: Introductionmentioning

confidence: 99%

Biased Tracer Reconstruction with Halo Mass Information

Liu

2021

ApJS

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Plenty of crucial information about our universe is encoded in the cosmic large-scale structure (LSS). However, extractions of this information are usually hindered by the nonlinearities of the LSS, which can be largely alleviated by various techniques known as reconstruction. In realistic applications, the efficiencies of these methods are always degraded by many limiting factors, a quite important one being the shot noise induced by the finite number density of biased matter tracers (i.e., luminous galaxies or dark matter halos) in observations. In this work, we explore the gains of biased tracer reconstruction achieved from halo mass information, which can suppress the shot-noise component and dramatically improves the cross-correlation between tracer field and dark matter. To this end, we first closely study the clustering biases and the stochasticity properties of halo fields with various number densities under different weighting schemes, i.e., the uniform, mass, and optimal weightings. Then, we apply the biased tracer reconstruction method to these different weighted halo fields and investigate how linear bias and observational mass scatter affect the reconstruction performance. Our results demonstrate that halo masses are critical information for significantly improving the performance of biased tracer reconstruction, indicating great application potential for substantially promoting the precision of cosmological measurements (especially for baryon acoustic oscillations) in ambitious ongoing and future galaxy surveys.

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The reconstructed power spectrum in the Zeldovich approximation

Cited by 33 publications

References 71 publications

Extracting high-order cosmological information in galaxy surveys with power spectra

Extracting high-order cosmological information in galaxy surveys with power spectra

barry and the BAO model comparison

Biased Tracer Reconstruction with Halo Mass Information

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