Superclusters with thermal Sunyaev--Zel'dovich effect surveys

Diaferio, Antonaldo; Nusser, Adi; Yoshida, Naoki; Sunyaev, R.

doi:10.1046/j.1365-8711.2003.06060.x

Cited by 11 publications

(27 citation statements)

References 63 publications

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“…The scale dependence of the nonlinear bias in the fitting functions provided by Hamana et al (2001), Diaferio et al (2003), Tinker et al (2005) is too weak to explain the clustering of haloes at the redshifts, masses and scales of interest here. The fitting function of Reed et al (2009) has a stronger scale dependence and describes the nonlinear clustering of high redshift MS-W7 haloes correctly.…”

Section: Comparing Simulations and Linear Bias Modelsmentioning

confidence: 85%

“…Earlier studies focused on the nonlinear bias of haloes at the present epoch (Hamana et al 2001;Diaferio et al 2003;Tinker et al 2005) or at redshifts before reionization (Reed et al 2009). The scale dependence of the nonlinear bias in the fitting functions provided by Hamana et al (2001), Diaferio et al (2003), Tinker et al (2005) is too weak to explain the clustering of haloes at the redshifts, masses and scales of interest here.…”

Section: Comparing Simulations and Linear Bias Modelsmentioning

confidence: 99%

“…2, the scale-dependent bias at z = 3 is expressed as a function of ξ sim mm (r, z) at the same redshift. This choice has been made in several analytic and numerical studies probing the clustering of dark matter haloes on quasi-linear scales (Hamana et al 2001;Scannapieco & Barkana 2002;Diaferio et al 2003;Tinker et al 2005;Reed et al 2009;van den Bosch et al 2013). These studies present the scale-dependent bias, b nl (r, M, z) as a function of the nonlinear dark matter correlation function, ξ sim mm (r, z) (r, z).…”

Section: The Measured Biasmentioning

confidence: 99%

“…There have been studies in the literature of deviations from the linear bias approximation on quasi-linear scales using analytic techniques (Scannapieco & Barkana 2002;Iliev et al 2003;Scannapieco & Thacker 2005) as well as N-body simulations (Hamana et al 2001;Diaferio et al 2003;Cen et al 2004;Gao et al 2005b;Tinker et al 2005;Angulo et al 2008;Reed et al 2009;van den Bosch et al 2013). In particular, these studies focused on the clustering of dark matter haloes either in the local universe (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The clustering of dark matter haloes: scale-dependent bias on quasi-linear scales

Jose¹,

Lacey²,

Baugh³

2016

Mon. Not. R. Astron. Soc.

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Section: Comparing Simulations and Linear Bias Modelsmentioning

confidence: 85%

Section: Comparing Simulations and Linear Bias Modelsmentioning

confidence: 99%

Section: The Measured Biasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The clustering of dark matter haloes: scale-dependent bias on quasi-linear scales

Jose¹,

Lacey²,

Baugh³

2016

Mon. Not. R. Astron. Soc.

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“…The angular function has been extensively studied by Diaferio et al (2003), while three dimensional clustering issues are elaborated by Moscardini et al (2002). Specifically, we explored how joint measurements of the counts and angular function could be used to constrain the cosmological parameters σ 8 and Ω M , when the normalization of the mass-temperature relation for clusters is known.…”

Section: Introductionmentioning

confidence: 99%

Cosmological constraints from a 2D SZ catalog

Mei

Bartlett

2004

A&A

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Abstract. We perform a Fisher matrix analysis to quantify cosmological constraints obtainable from a 2-dimensional Sunyaev-Zel'dovich (SZ) cluster catalog using the counts and the angular correlation function. Three kinds of SZ survey are considered: the almost all-sky Planck survey and two deeper ground-based surveys, one with 10% sky coverage, the other one with a coverage of 250 square degrees. With the counts and angular function, and adding the constraint from the local X-ray cluster temperature function, joint 10% to 30% errors (1σ) are achievable on the cosmological parameter pair (σ 8 , Ω M ) in the flat concordance model. Constraints from a 2D distribution remain relatively robust to uncertainties in possible cluster gas evolution for the case of Planck. Alternatively, we examine constraints on cluster gas physics when assuming priors on the cosmological parameters (e.g., from cosmic microwave background anisotropies and SNIa data), finding a poor ability to constrain gas evolution with the 2-dimensional catalog. From just the SZ counts and angular correlation function we obtain, however, a constraint on the product between the present-day cluster gas mass fraction and the normalization of the mass-temperature relation, T * , with a precision of 15%. This is particularly interesting because it would be based on a very large catalog and is independent of any X-ray data.

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Mass functions and bias of dark matter halos

Valageas¹

2009

A&A

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Aims. We revisit the study of the mass functions and the bias of dark matter halos. Methods. Focusing on the limit of rare massive halos, we point out that exact analytical results can be obtained for the large-mass tail of the halo mass function. This is most easily seen from a steepest-descent approach, that becomes asymptotically exact for rare events. We also revisit the traditional derivation of the bias of massive halos, associated with overdense regions in the primordial density field. Results. We check that the theoretical large-mass cutoff agrees with the mass functions measured in numerical simulations. For halos defined by a nonlinear threshold δ = 200 this corresponds to using a linear threshold δ L 1.59 instead of the traditional value 1.686. We also provide a fitting formula that matches simulations over all mass scales and obeys the exact large-mass tail. Next, paying attention to the Lagrangian-Eulerian mapping (i.e. corrections associated with the motions of halos), we improve the standard analytical formula for the bias of massive halos. We check that our prediction, which contains no free parameter, agrees reasonably well with numerical simulations. In particular, it recovers the steepening of the dependence on scale of the bias that is observed at higher redshifts, which published fitting formulae did not capture. This behavior mostly arises from nonlinear biasing.

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Superclusters with thermal Sunyaev--Zel'dovich effect surveys

Cited by 11 publications

References 63 publications

The clustering of dark matter haloes: scale-dependent bias on quasi-linear scales

The clustering of dark matter haloes: scale-dependent bias on quasi-linear scales

Cosmological constraints from a 2D SZ catalog

Mass functions and bias of dark matter halos

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