Local gravity versus local velocity: solutions for β and non-linear bias

被引:141
|
作者
Davis, Marc [1 ,2 ]
Nusser, Adi [3 ,4 ]
Masters, Karen L. [5 ]
Springob, Christopher [6 ]
Huchra, John P. [7 ]
Lemson, Gerard [8 ]
机构
[1] Univ Calif Berkeley, Dept Astron, Berkeley, CA 94720 USA
[2] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA
[3] Technion Israel Inst Technol, Dept Phys, IL-32000 Haifa, Israel
[4] Technion Israel Inst Technol, Asher Space Sci Inst, IL-32000 Haifa, Israel
[5] Univ Portsmouth, Inst Cosmol & Gravitat, Portsmouth PO1 3FX, Hants, England
[6] Anglo Australian Observ, Epping, NSW 1710, Australia
[7] Harvard Smithsonian Ctr Astrophys, Cambridge, MA 02138 USA
[8] Max Planck Inst Astrophys, D-85741 Garching, Germany
基金
以色列科学基金会; 美国国家科学基金会;
关键词
cosmological parameters; dark matter; large-scale structure of Universe; TULLY-FISHER RELATION; GALAXY REDSHIFT SURVEY; SFI PECULIAR VELOCITIES; DIGITAL SKY SURVEY; 100 H(-1) MPC; COSMOLOGICAL PARAMETERS; CLUSTER GALAXIES; IRAS-GALAXIES; BULK FLOW; IA SUPERNOVAE;
D O I
10.1111/j.1365-2966.2011.18362.x
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
We perform a reconstruction of the cosmological large-scale flows in the nearby Universe using two complementary observational sets. The first, the SFI++ sample of Tully-Fisher (TF) measurements of galaxies, provides a direct probe of the flows. The second, the whole sky distribution of galaxies in the 2MASS (Two Micron All Sky Survey) redshift survey (2MRS), yields a prediction of the flows given the cosmological density parameter, , and a biasing relation between mass and galaxies. We aim at an unbiased comparison between the peculiar velocity fields extracted from the two data sets and its implication on the cosmological parameters and the biasing relation. We expand the fields in a set of orthonormal basis functions, each representing a plausible realization of a cosmological velocity field smoothed in such a way as to give a nearly constant error on the derived SFI++ velocities. The statistical analysis is done on the coefficients of the modal expansion of the fields by means of the basis functions. Our analysis completely avoids the strong error covariance in the smoothed TF velocities by the use of orthonormal basis functions and employs elaborate mock data sets to extensively calibrate the errors in 2MRS predicted velocities. We relate the 2MRS galaxy distribution to the mass density field by a linear bias factor, b, and include a luminosity-dependent, proportional to L alpha, galaxy weighting. We assess the agreement between the fields as a function of alpha and beta = f()/b, where f is the growth factor of linear perturbations. The agreement is excellent with a reasonable chi 2 per degree of freedom. For alpha = 0, we derive 0.28 < beta < 0.37 and 0.24 < beta < 0.43, respectively, at the 68.3 per cent and 95.4 per cent confidence levels (CLs). For beta = 0.33, we get alpha < 0.25 and alpha < 0.5, respectively, at the 68.3 per cent and 95.4 per cent CLs. We set a constraint on the fluctuation normalization, finding Sigma(8) = 0.66 +/- 0.10, which is only 1.5 Sigma deviant from Wilkinson Microwave Anisotropy Probe (WMAP) results. It is remarkable that Sigma(8) determined from this local cosmological test is close to the value derived from the cosmic microwave background, an indication of the precision of the standard model.
引用
收藏
页码:2906 / 2922
页数:17
相关论文
共 50 条
  • [41] The non-linear matter and velocity power spectra in f(R) gravity
    Li, Baojiu
    Hellwing, Wojciech A.
    Koyama, Kazuya
    Zhao, Gong-Bo
    Jennings, Elise
    Baugh, Carlton M.
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2013, 428 (01) : 743 - 755
  • [42] On the effective action in presence of local non-linear constraints
    Rancon, Adam
    Balog, Ivan
    JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2019,
  • [43] LOCAL-CONTROLLABILITY FOR AUTONOMOUS NON-LINEAR SYSTEMS
    TIBERIO, RMB
    ZECCA, P
    JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 1980, 31 (01) : 69 - 83
  • [44] Non-linear CCA and PCA by alignment of local models
    Verbeek, JJ
    Roweis, ST
    Vlassis, N
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16, 2004, 16 : 297 - 304
  • [45] Non-linear subdivision using local spherical coordinates
    Aspert, N
    Ebrahimi, T
    Vandergheynst, P
    COMPUTER AIDED GEOMETRIC DESIGN, 2003, 20 (03) : 165 - 187
  • [46] LOCAL-CONTROLLABILITY OF NON-LINEAR SYSTEMS - AN EXAMPLE
    STEFANI, G
    SYSTEMS & CONTROL LETTERS, 1985, 6 (02) : 123 - 125
  • [47] A Local Online Learning Approach for Non-linear Data
    Yang, Xinxing
    Zhou, Jun
    Zhao, Peilin
    Chen, Cen
    Chen, Chaochao
    Li, Xiaolong
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2018, PT II, 2018, 10938 : 429 - 441
  • [48] Identification of non-linear processes with dynamic local-affine models - Measurements to the reduction of bias and variance
    Zimmerschied, Ralf
    AT-AUTOMATISIERUNGSTECHNIK, 2009, 57 (04) : 219 - 219
  • [49] Non-Linear Feature Extraction by Linear PCA Using Local Kernel
    Hotta, Kazuhiro
    19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6, 2008, : 2064 - 2067
  • [50] Integrable local and non-local vector Non-linear Schrodinger Equation with balanced loss and gain
    Sinha, Debdeep
    PHYSICS LETTERS A, 2022, 448