BAO reconstruction: a swift numerical action method for massive spectroscopic surveys

Sarpa, Elena; Schimd, C.; Branchini, E.; Matarrese, S.

doi:10.1093/mnras/stz278

Cited by 36 publications

(32 citation statements)

References 86 publications

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“…Peebles' algorithm was made more efficient for application to larger datasets but lacked uniqueness: existence of mulitple minima, maxima and saddle points in the landscape of solutions lead to multiple trajectories, all physically viable (Peebles 1994;Shaya et al 1995;Nusser & Branchini 2000;Branchini et al 2002). Most recently, the method's excess complexity has been reduced in new numerical schemes and also by smoothing out strongly non-linear scales (Sarpa et al 2019(Sarpa et al , 2020.…”

Section: Introductionmentioning

confidence: 99%

A fast semidiscrete optimal transport algorithm for a unique reconstruction of the early Universe

Lévy

Mohayaee

Hausegger

2021

Monthly Notices of the Royal Astronomical Society

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We leverage powerful mathematical tools stemming from optimal transport theory and transform them into an efficient algorithm to reconstruct the fluctuations of the primordial density field, built on solving the Monge-Ampère-Kantorovich equation. Our algorithm computes the optimal transport between an initial uniform continuous density field, partitioned into Laguerre cells, and a final input set of discrete point masses, linking the early to the late Universe. While existing early universe reconstruction algorithms based on fully discrete combinatorial methods are limited to a few hundred thousand points, our algorithm scales up well beyond this limit, since it takes the form of a well-posed smooth convex optimization problem, solved using a Newton method. We run our algorithm on cosmological N-body simulations, from the AbacusCosmos suite, and reconstruct the initial positions of $\mathcal {O}(10^7)$ particles within a few hours with an off-the-shelf personal computer. We show that our method allows a unique, fast and precise recovery of subtle features of the initial power spectrum, such as the baryonic acoustic oscillations.

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Section: Introductionmentioning

confidence: 99%

A fast semidiscrete optimal transport algorithm for a unique reconstruction of the early Universe

Lévy

Mohayaee

Hausegger

2021

Monthly Notices of the Royal Astronomical Society

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“…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 Sloan Digital Sky Survey (SDSS) [3], the DEEP2 Galaxy Redshift Survey (DEEP2) [4,5], and the VIMOS Public Extragalactic Redshift Survey (VIPERS) [6] have provided tremendous measurements of the galaxy spatial distributions, which enable us to explore precise cosmological parameters. The Baryon Acoustic Oscillations (BAO) [7][8][9][10][11], gravitional lensing [12][13][14][15][16][17][18][19][20], power spectrum [21][22][23][24][25][26][27][28][29] and correaltion functions (such as two-point correlation functions (2PCF) and three-point correlation functions (3PCF) [30][31][32] are often used to depict the universe. The 2PCF, a second-order moment statistics, or its integrated version, the projected 2PCF, are usually employed to characterize the clustering strength of galaxies.…”

Section: Introductionmentioning

confidence: 99%

The clustering of galaxies in the DESI imaging legacy surveys DR8: I. the luminosity and color dependent intrinsic clustering

Wang¹,

Xu²,

Yang³

et al. 2021

Preprint

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In a recent study, we developed a method to model the impact of photometric redshift uncertainty on the two-point correlation function (2PCF). In this method, we can obtain both the intrinsic clustering strength and the photometric redshift errors simultaneously by fitting the projected 2PCF with two integration depths along the line-of-sight. Here we apply this method to the DESI Legacy Imaging Surveys Data Release 8 (LS DR8), the largest galaxy sample currently available. We separate galaxies into 20 samples in 8 redshift bins from z = 0.1 to z = 1.0, and a few z-band absolute magnitude bins, with M z ≤ −20. These galaxies are further separated into red and blue sub-samples according to their M 0.5 r − M 0.5 z colors. We measure the projected 2PCFs for all these galaxy (sub-)samples, and fit them using our photometric redshift 2PCF model. We find that the photometric redshift errors are smaller in red sub-samples than the overall population. On the other hand, there might be some systematic photometric redshift errors in the blue sub-samples, so that some of the sub-samples show significantly enhanced 2PCF at large scales. Therefore, focusing only on the red and all (sub-)samples, we find that the biases of galaxies in these (sub-)samples show clear color, redshift and luminosity dependencies, in that red brighter galaxies at higher redshift are more biased than their bluer and low redshift counterparts. Apart from the best fit set of parameters, σ z and b, from this state-of-the-art photometric redshift survey, we obtain high precision intrinsic clustering measurements for these 40 red and all galaxy (sub-)samples. These measurements on large and small scales hold important information regarding the cosmology and galaxy formation, which will be used in our subsequent probes in this series.

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BAO reconstruction: a swift numerical action method for massive spectroscopic surveys

Cited by 36 publications

References 86 publications

A fast semidiscrete optimal transport algorithm for a unique reconstruction of the early Universe

A fast semidiscrete optimal transport algorithm for a unique reconstruction of the early Universe

Biased Tracer Reconstruction with Halo Mass Information

The clustering of galaxies in the DESI imaging legacy surveys DR8: I. the luminosity and color dependent intrinsic clustering

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