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THE NEW HORIZON RUN COSMOLOGICAL N-BODY SIMULATIONS

  • Kim, Ju-Han (Center for Advanced Computation, Korea Institute for Advanced Study) ;
  • Park, Chang-Bom (School of Physics, Korea Institute for Advanced Study) ;
  • Rossi, Graziano (School of Physics, Korea Institute for Advanced Study) ;
  • Lee, Sang-Min (Supercomputing Center, KISTI) ;
  • Gott, J. Richard III (Department of Astrophysical Sciences, Princeton University)
  • Received : 2011.11.09
  • Accepted : 2011.12.02
  • Published : 2011.12.31

Abstract

We present two large cosmological N-body simulations, called Horizon Run 2 (HR2) and Horizon Run 3 (HR3), made using $6000^3$ = 216 billions and $7210^3$ = 374 billion particles, spanning a volume of $(7.200\;h^{-1}Gpc)^3$ and $(10.815\;h^{-1}Gpc)^3$, respectively. These simulations improve on our previous Horizon Run 1 (HR1) up to a factor of 4.4 in volume, and range from 2600 to over 8800 times the volume of the Millennium Run. In addition, they achieve a considerably finer mass resolution, down to $1.25{\times}10^{11}h^{-1}M_{\odot}$, allowing to resolve galaxy-size halos with mean particle separations of $1.2h^{-1}$Mpc and $1.5h^{-1}$Mpc, respectively. We have measured the power spectrum, correlation function, mass function and basic halo properties with percent level accuracy, and verified that they correctly reproduce the CDM theoretical expectations, in excellent agreement with linear perturbation theory. Our unprecedentedly large-volume N-body simulations can be used for a variety of studies in cosmology and astrophysics, ranging from large-scale structure topology, baryon acoustic oscillations, dark energy and the characterization of the expansion history of the Universe, till galaxy formation science - in connection with the new SDSS-III. To this end, we made a total of 35 all-sky mock surveys along the past light cone out to z = 0.7 (8 from the HR2 and 27 from the HR3), to simulate the BOSS geometry. The simulations and mock surveys are already publicly available at http://astro.kias.re.kr/Horizon-Run23/.

Acknowledgement

Supported by : Korea Institute of Science and Technology Information

References

  1. Aarseth, S. J., Turner, E. L., & Gott, J. R. 1979, N-Body Simulations of Galaxy Clustering. I - Initial Conditions and Galaxy Collapse Times, ApJ, 228, 664 https://doi.org/10.1086/156892
  2. Abbott, T., et al. 2005, The Dark Energy Survey, astro-ph/0510346
  3. Albrecht, A., et al. 2006, Report of the Dark Energy Task Force, astro-ph/0609591
  4. Bertschinger, E. 1998, Simulations of Structure Formation in the Universe, ARAA, 36, 599 https://doi.org/10.1146/annurev.astro.36.1.599
  5. Bett, P., Eke, V., Frenk, C. S., Jenkins, A., Helly, J., & Navarro, J. 2007, The Spin and Shape of Dark Matter Haloes in the Millennium Simulation of a  Cold Dark Matter Universe, MNRAS, 376, 215 https://doi.org/10.1111/j.1365-2966.2007.11432.x
  6. Blake, C., et al. 2008, The Wiggle Z Dark Energy Survey, Astronomy & Geophysics, 49, 19
  7. Bode, P., Bahcall, N. A., Ford, E. B., & Ostriker, J. P. 2001, Evolution of the Cluster Mass Function: GPC3 Dark Matter Simulations, ApJ, 551, 15 https://doi.org/10.1086/320077
  8. Carlberg, R. G., & Couchman, H. M. P. 1989, Mergers and Bias in a Cold Dark Matter Cosmology, ApJ, 340, 47 https://doi.org/10.1086/167375
  9. Carnero, A., Sanchez, E., Crocce, M., Cabre, A., & Gaztanaga, E. 2011, Clustering of Photometric Luminous Red Galaxies - II. Cosmological Implications from the Baryon Acoustic Scale, arXiv:1104.5426
  10. Choi, Y.-Y., Park, C., Kim, J., Gott, J. R., Weinberg, D. H., Vogeley, M. S., & Kim, S. S. 2010, Galaxy Clustering Topology in the Sloan Digital Sky Survey Main Galaxy Sample: A Test for Galaxy Formation Models, ApJS, 190, 181 https://doi.org/10.1088/0067-0049/190/1/181
  11. Cimatti, A., et al. 2008, GMASS Ultradeep Spec- troscopy of Galaxies at z -2. II. Superdense Passive Galaxies: How Did They Form and Evolve?, A&A, 482, 21 https://doi.org/10.1051/0004-6361:20078739
  12. Colberg, J. M., White, S. D. M., Yoshida, N., et al. 2000, Clustering of Galaxy Clusters in Cold Dark Matter Universes, MNRAS, 319, 209 https://doi.org/10.1046/j.1365-8711.2000.03832.x
  13. Cole, S., et al. 2005, The 2dF Galaxy Redshift Survey: Power-Spectrum Analysis of the Final Data Set and Cosmological Implications, MNRAS, 362, 505 https://doi.org/10.1111/j.1365-2966.2005.09318.x
  14. Colless, M., et al. 2001, The 2dF Galaxy Redshift Sur- vey: Spectra and Redshifts, MNRAS, 328, 1039 https://doi.org/10.1046/j.1365-8711.2001.04902.x
  15. Crocce, M., & Scoccimarro, R. 2008, Nonlinear Evolu- tion of Baryon Acoustic Oscillations, Phys. Rev. D., 77, 3533
  16. Crocce, M., Pueblas, S., & Scoccimarro, R. 2006, Transients from Initial Conditions in Cosmological Simulations, MNRAS, 373, 369 https://doi.org/10.1111/j.1365-2966.2006.11040.x
  17. Crotts, A., et al. 2005, Joint Efficient Dark-energy Investigation (JEDI): a Candidate Implementation of the NASA-DOE Joint Dark Energy Mission (JDEM), astro-ph/0507043
  18. Dalal, N., Dore, O., Huterer, D., & Shirokov, A. 2008, Imprints of Primordial Non-Gaussianities on Large-Scale Structure: Scale-Dependent Bias and Abundance of Virialized Objects, Phys. Rev. D., 77, 123514 https://doi.org/10.1103/PhysRevD.77.123514
  19. Davis, M., Efstathiou, G., Frenk, C. S., & White, S. D. M. 1985, The Evolution of Large-Scale Struc- ture in a Universe Dominated by Cold Dark Matter, ApJ, 292, 371 https://doi.org/10.1086/163168
  20. Desjacques, V., & Seljak, U. 2010, Signature of Primor- dial Non-Gaussianity of 3 Type in the Mass Func- tion and Bias of Dark Mtter Haloes, Phys. Rev. D., 81, 3006
  21. Desjacques, V., Seljak, U., & Iliev, I. T. 2009, Scale- Dependent Bias Induced by Local Non-Gaussianity: a Comparison to N-Body Simulations, MNRAS, 396, 85 https://doi.org/10.1111/j.1365-2966.2009.14721.x
  22. Diemand, J., & Moore, B. 2009, The Structure and Evolution of Cold Dark Matter Halos, arXiv:0906.4340
  23. Diemand, J., Kuhlen, M., Madau, P., Zemp, M., Moore, B., Potter, D., & Stadel, J. 2008, Clumps and Streams in the Local Dark Matter Distribution, Nature, 454, 735 https://doi.org/10.1038/nature07153
  24. Diemand, J., Moore, B., & Stadel, J. 2004, Convergence and Scatter of Cluster Density Profiles, MNRAS, 353, 624 https://doi.org/10.1111/j.1365-2966.2004.08094.x
  25. Dubinski, J., Kim, J., Park, C., & Humble, R. 2004, GOTPM: a Parallel Hybrid Particle-Mesh Treecode, New Astronomy, 9, 111 https://doi.org/10.1016/j.newast.2003.08.002
  26. Efstathiou, G., & Eastwood, J. W. 1981, On the Clustering of Particles in an Expanding Universe, MNRAS, 194, 503 https://doi.org/10.1093/mnras/194.3.503
  27. isenstein, D. J., et al. 2005, Detection of the Baryon Acoustic Peak in the Large-Scale Correlation Function of SDSS Luminous Red Galaxies, ApJ, 633, 560 https://doi.org/10.1086/466512
  28. Eisenstein, D. J., & Hu, W. 1999, Power Spectra for Cold Dark Matter and Its Variants, ApJ, 511, 5 https://doi.org/10.1086/306640
  29. isenstein, D. J., & Hu, W. 1998, Baryonic Features in the Matter Transfer Function, ApJ, 496, 605 https://doi.org/10.1086/305424
  30. Gao, L., et al. 2008, The Redshift Dependence of the Structure of Massive Cold Dark Matter Haloes, MNRAS, 387, 536 https://doi.org/10.1111/j.1365-2966.2008.13277.x
  31. Gao, L., & White, S. D. M. 2007, Assembly Bias in the Clustering of Dark Matter Haloes, MNRAS, 377, 5 https://doi.org/10.1111/j.1745-3933.2007.00292.x
  32. Gaztanaga, E., Cabre, A., & Hui, L. 2009, Clustering of Luminous Red Galaxies - IV. Baryon Acoustic Peak in the Line-of-Sight Direction and a Direct Measurement of H(z), MNRAS, 399, 1663 https://doi.org/10.1111/j.1365-2966.2009.15405.x
  33. elb, J. M., & Bertschinger, E. 1994, Cold Dark Matter. 1: The Formation of Dark Halos, ApJ, 436, 467 https://doi.org/10.1086/174922
  34. Gott, J. R., Choi, Y.-Y., Park, C., & Kim, J. 2009, Three-Dimensional Genus Topology of Luminous Red Galaxies, ApJ, 695, 45
  35. Gott, J. R., et al. 2008, Genus Topology of Structure in the Sloan Digital Sky Survey: Model Testing, ApJ, 675, 16
  36. Gott, J. R., Juric, M., Schlegel, D., Hoyle, F., Vogeley, M., Tegmark, M., Bahcall, N., & Brinkmann, J. 2005, A Map of the Universe, ApJ, 624, 463 https://doi.org/10.1086/428890
  37. Gott, J. R., Dickinson, M., & Melott, A. L. 1986, The Sponge-Like Topology of Large-Scale Structure in the Universe, ApJ, 306, 341 https://doi.org/10.1086/164347
  38. Governato, F., Babul, A., Quinn, T., et al. 1999, Properties of Galaxy Clusters: Mass and Correlation Functions, MNRAS, 307, 949 https://doi.org/10.1046/j.1365-8711.1999.02706.x
  39. Green, J., et al. 2011, Wide-Field InfraRed Survey Telescope (WFIRST) Interim Report, arXiv: 1108.1374
  40. Groth, E. J., & Peebles, P. J. E. 1975, N-Body Studies of the Clustering of Galaxies, BAAS, 7, 425
  41. Henon, M., & Heiles, C. 1964, The Applicability of the Third Integral of Motion: Some Numerical Experiments, AJ, 69, 73 https://doi.org/10.1086/109234
  42. Hill, G. J., Gebhardt, K., Komatsu, E., & MacQueen, P. J. 2004, The Hobby-Eberly Telescope Dark Energy Experiment, AIPC, 743, 224
  43. Jee, I., Park, C., & Kim, J. 2011, A Second-Order Bias Model for the Logarithmic Halo Mass Density, ApJ, submitted
  44. nkins, A., et al. 2001, The Mass Function of Dark Matter Haloes, MNRAS, 321, 372 https://doi.org/10.1046/j.1365-8711.2001.04029.x
  45. Jenkins, A., Frenk, C. S., Pearce, F. R., et al. 1998, Evolution of Structure in Cold Dark Matter Universes, ApJ, 499, 20 https://doi.org/10.1086/305615
  46. Jeong, D., & Komatsu, E. 2009, Primordial Non- Gaussianity, Scale-dependent Bias, and the Bispec- trum of Galaxies, ApJ, 703, 1230 https://doi.org/10.1088/0004-637X/703/2/1230
  47. ing, Y. P., Suto, Y., & Mo, H. J. 2007, The Dependence of Dark Halo Clustering on Formation Epoch and Concentration Parameter, ApJ, 657, 664
  48. ing, Y. P., & Suto, Y. 2002, Triaxial Modeling of Halo Density Profiles with High-Resolution N-Body Simulations, ApJ, 574, 538
  49. Kaiser, N., et al. 2002, Pan-STARRS: A Large Synoptic Survey Telescope Array, Proc. SPIE, 4836, 154
  50. Kazin, E. A., Blanton, M. R., Scoccimarro, R., McBride, C. K., & Berlind, A. A. 2010, The Baryonic Acoustic Feature and Large-Scale Clustering in the Sloan Digital Sky Survey Luminous Red Galaxy Sample, ApJ, 710, 1444 https://doi.org/10.1088/0004-637X/710/2/1444
  51. Kim, J., Park, C., Gott, J. R., & Dubinski, J. 2009, The Horizon Run N-Body Simulation: Baryon Acoustic Oscillations and Topology of Large-scale Structure of the Universe, ApJ, 701, 1547 https://doi.org/10.1088/0004-637X/701/2/1547
  52. Kim, J., Park, C., & Choi, Y.-Y. 2008, A Subhalo- Galaxy Correspondence Model of Galaxy Biasing, ApJ, 683, 123 https://doi.org/10.1086/589566
  53. im, J., & Park, C. 2006, A New Halo-Finding Method for N-Body Simulations, ApJ, 639, 600 https://doi.org/10.1086/499761
  54. Klypin, A., Kravtsov, A. V., Bullock, J. S., & Primack, J. R. 2001, Resolving the Structure of Cold Dark Matter Halos, ApJ, 554, 903
  55. Komatsu, E., et al. 2011, Seven-year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation, ApJS, 192, 18 https://doi.org/10.1088/0067-0049/192/2/18
  56. Komatsu, E., et al. 2009, Five-Year Wilkinson Microwave Anisotropy Probe Observations: Cosmological Interpretation, ApJS, 180, 330 https://doi.org/10.1088/0067-0049/180/2/330
  57. Kowalski, M., et al. 2008, Improved Cosmological Constraints from New, Old, and Combined Supernova Data Sets, ApJ, 686, 749 https://doi.org/10.1086/589937
  58. Li, Y., Mo, H. J., & Gao, L. 2008, On Halo Formation Times and Assembly Bias, MNRAS, 389, 1419 https://doi.org/10.1111/j.1365-2966.2008.13667.x
  59. LoVerde, M., Hui, L., & Gaztanaga, E. 2008, Lensing Corrections to Features in the Angular Two-Point Correlation Function and Power Spectrum, Phys. Rev. D., 77, 3512
  60. Lukic, J., Heitmann, K., Habib, S., Bashinsky, S., & Ricker, P. M. 2007, The Halo Mass Function: High- Redshift Evolution and Universality, ApJ, 671, 1160 https://doi.org/10.1086/523083
  61. Maccio, A. V., Dutton, A. A., van den Bosch, F. C., Moore, B., Potter, D., & Stadel, J. 2007, Concentration, Spin and Shape of Dark Matter Haloes: Scatter and the Dependence on Mass and Environment, MNRAS, 378, 55 https://doi.org/10.1111/j.1365-2966.2007.11720.x
  62. Matsubara, T. 2004, Correlation Function in Deep Redshift Space as a Cosmological Probe, ApJ, 615, 573 https://doi.org/10.1086/424561
  63. Miyoshi, K., & Kihara, T. 1975, Development of the Correlation of Galaxies in an Expanding Universe, PASJ, 27, 333
  64. Montesano, F., Sanchez, A. G., & Phleps, S. 2010, A New Model for the Full Shape of the Large-Scale Power Spectrum, MNRAS, 408, 2397 https://doi.org/10.1111/j.1365-2966.2010.17292.x
  65. Moore, B., Governato, F., Quinn, T., Stadel, J., & Lake, G. 1998, Resolving the Structure of Cold Dark Matter Halos, ApJ, 499, 5 https://doi.org/10.1086/311333
  66. Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, A Universal Density Profile from Hierarchical Clustering, ApJ, 490, 493 https://doi.org/10.1086/304888
  67. Navarro, J. F., Frenk, C. S., & White, S. D. M. 1996, The Structure of Cold Dark Matter Halos, ApJ, 462, 563 https://doi.org/10.1086/177173
  68. Neto, A., et al. 2007, The Statistics of CDM Halo Concentrations, MNRAS, 381, 1450 https://doi.org/10.1111/j.1365-2966.2007.12381.x
  69. Park, C. 1997, A Particle-Mesh Code for the Next Generation Cosmological N-Body Simulations, JKAS, 30, 191
  70. Park, C. 1990, Large N-Body Simulations of a Universe Dominated by Cold Dark Matter MNRAS, 242, 59 https://doi.org/10.1093/mnras/242.1.59
  71. Park C., & Kim, Y. R. 2010, Large-Scale Structure of the Universe as a Cosmic Standard Ruler, ApJL, 715, L185 https://doi.org/10.1088/2041-8205/715/2/L185
  72. Park, C., Kim, J., & Gott, J. R. 2005, Effects of Gravitational Evolution, Biasing, and Redshift Space Distortion on Topology, ApJ, 633, 1 https://doi.org/10.1086/452621
  73. Park, C., Colley,W. N., Gott, J. R., Ratra, B., Spergel, D. N., & Sugiyama, N. 1998, Cosmic Microwave Background Anisotropy Correlation Function and Topology from Simulated Maps for MAP, ApJ, 506, 473 https://doi.org/10.1086/306259
  74. Park, C., Vogeley, M. S., Geller, M. J., & Huchra, J. P. 1994, Power Spectrum, Correlation Function, and Tests for Luminosity Bias in the CfA Redshift Survey, ApJ, 431, 569 https://doi.org/10.1086/174508
  75. Park, C., & Gott, J. R. 1991, Simulation of Deep One-and Two-Dimensional Redshift Surveys, MNRAS, 249, 288 https://doi.org/10.1093/mnras/249.2.288
  76. Peebles, P. J. E. 1982, The Peculiar Velocity around a Hole in the Galaxy Distribution, ApJ, 257, 438 https://doi.org/10.1086/160001
  77. Peebles, P. J. E. 1970, Structure of the Coma Cluster of Galaxies, ApJ, 75, 13 https://doi.org/10.1086/110933
  78. Percival, W. J., et al. 2010, Baryon Acoustic Oscillations in the Sloan Digital Sky Survey Data Release 7 galaxy Sample, MNRAS, 401, 2148 https://doi.org/10.1111/j.1365-2966.2009.15812.x
  79. Percival, W. J., Cole, S., Eisenstein, D. J., Nichol, R. C., Peacock, J. A., Pope, A. C., & Szalay, A. S. 2007, Measuring the Baryon Acoustic Oscillation scale using the Sloan Digital Sky Survey and 2dF Galaxy Redshift Survey, MNRAS, 381, 1053 https://doi.org/10.1111/j.1365-2966.2007.12268.x
  80. Perlmutter, S., et al. 1999, Measurements of Omega and Lambda from 42 High-Redshift Supernovae, ApJ, 517, 565 https://doi.org/10.1086/307221
  81. Press, W. H., & Schechter, P. 1974, Formation of Galaxies and Clusters of Galaxies by Self-Similar Gravitational Condensation, ApJ, 187, 425 https://doi.org/10.1086/152650
  82. Reed, D., et al. 2005, Evolution of the Density Profiles of Dark Matter Haloes, MNRAS, 357, 82 https://doi.org/10.1111/j.1365-2966.2005.08612.x
  83. Reid, B. A., et al. 2010, Cosmological Constraints from the Clustering of the Sloan Digital Sky Survey DR7 Luminous Red Galaxies, MNRAS, 404, 60 https://doi.org/10.1111/j.1745-3933.2010.00835.x
  84. Riess, A. G., et al. 1998, Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant, AJ, 116, 1009 https://doi.org/10.1086/300499
  85. Sanchez, A. G., Crocce, M., Cabre, A., Baugh, C. M., & Gaztanaga, E. 2009, Cosmological Parameter Constraints from SDSS Luminous Red Galaxies: a New Treatment of Large-Scale Clustering, MNRAS, 400, 1643 https://doi.org/10.1111/j.1365-2966.2009.15572.x
  86. Sanchez, A. G., et al. 2006, Cosmological Parameters from Cosmic Microwave Background Measurements and the Final 2dF Galaxy Redshift Survey Power Spectrum, MNRAS, 366, 189 https://doi.org/10.1111/j.1365-2966.2005.09833.x
  87. Schlegel, D., et al. 2011, The BigBOSS Experiment, arXiv: 1106.1706
  88. Schlegel, D., White, M., & Eisenstein, D. 2009, The Baryon Oscillation Spectroscopic Survey: Precision measurement of the absolute cosmic distance scale, The Astronomy and Astrophysics Decadal Survey, Science White Papers, 314
  89. Shandarin, S., Habib, S., & Heitmann, K. 2010, Origin of the Cosmic Network in $\Lambda$CDM: Nature vs Nurture, Phys. Rev. D., 81, 3006
  90. Spergel, D. N., et al. 2003, First-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Determination of Cosmological Parameters, ApJS, 148, 175 https://doi.org/10.1086/377226
  91. Springel, V., et al. 2008, The Aquarius Project: the Subhaloes of Galactic Haloes, MNRAS, 391, 1685 https://doi.org/10.1111/j.1365-2966.2008.14066.x
  92. Springel, V., et al. 2005, Simulations of the Formation, Evolution and Clustering of Galaxies and Quasars, Nature, 435, 629 https://doi.org/10.1038/nature03597
  93. Stadel, J., et al. 2009, Quantifying the Heart of Darkness with GHALO - a Multibillion Particle Simulation of a Galactic Halo, MNRAS, 398, 21 https://doi.org/10.1111/j.1745-3933.2009.00699.x
  94. Sugiyama, N. 1995, Cosmic Background Anisotropies in Cold Dark Matter Cosmology, ApJS, 100, 281 https://doi.org/10.1086/192220
  95. Suto, Y., & Suginohara, T. 1991, Redshift-Space Correlation Functions in the Cold Dark Matter Scenario, ApJL, 370, L15 https://doi.org/10.1086/185966
  96. eyssier, R., et al. 2009, Full-Sky Weak-Lensing Simulation with 70 Billion Particles, A&A, 497, 335 https://doi.org/10.1051/0004-6361/200810657
  97. Tyson, J. A., & LSST 2004, The Large Synoptic Survey Telescope Science Requirements, AAS, 20510801
  98. van Albada, G. B. 1961, Evolution of Clusters of Galaxies under Gravitational Forces, AJ, 66, 590 https://doi.org/10.1086/108469
  99. Verde, L., & Matarrese, S. 2009, Detectability of the Effect of Inflationary Non-Gaussianity on Halo Bias, ApJ, 706, 91 https://doi.org/10.1088/0004-637X/706/1/L91
  100. ogeley, M. S., Park, C., Geller, M. J., & Huchra, J. P. 1992, Large-Scale Clustering of Galaxies in the CfA Redshift Survey, ApJ, 391, 5 https://doi.org/10.1086/186385
  101. Wambsganss, J., Bode, P., & Ostriker, J. P. 2004, Giant Arc Statistics in Concord with a Concordance Lambda Cold Dark Matter Universe, ApJL, 606, L93 https://doi.org/10.1086/421459
  102. Warren, M. S., Quinn, P. J., Salmon, J. K., & Zurek, W. H. 1992, Dark Halos Formed via Dissipationless Collapse. I - Shapes and Alignment of Angular Momentum, ApJ, 399, 405 https://doi.org/10.1086/171937
  103. White, S. D. M. 1976, The Dynamics of Rich Clusters of Galaxies, MNRAS, 177, 717 https://doi.org/10.1093/mnras/177.3.717
  104. White, S. D. M., Davis, M., Efstathiou, G., & Frenk, C. S. 1987, Galaxy Distribution in a Cold Dark Matter Universe, Nature, 330, 451 https://doi.org/10.1038/330451a0
  105. White, S. D. M., & Rees, M. J. 1978, Core Condensation in Heavy Halos - A Two-Stage Theory for Galaxy Formation and Clustering, MNRAS, 183, 341 https://doi.org/10.1093/mnras/183.3.341
  106. Yoo, J., Fitzpatrick, A. L., & Zaldarriaga, M. 2009, New Perspective on Galaxy Clustering as a Cos- mological Probe: General Relativistic Effects, Phys. Rev. D., 80, 3514
  107. York, D. G., et al. 2000, The Sloan Digital Sky Survey: Technical Summary, AJ, 120, 1579 https://doi.org/10.1086/301513
  108. Zheng, Z., & Weinberg, D. H. 2007, Breaking the Degeneracies between Cosmology and Galaxy Bias, ApJ, 659, 1 https://doi.org/10.1086/512151
  109. Zurek, W. H., Quinn, P. J., Salmon, J. K., & Warren, M. S. 1994, Large-Scale Structure after COBE: Peculiar Velocities and Correlations of Cold Dark Matter Halos, ApJ, 431, 559 https://doi.org/10.1086/174507

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  17. TOPOLOGY OF LUMINOUS RED GALAXIES FROM THE SLOAN DIGITAL SKY SURVEY vol.209, pp.2, 2013, https://doi.org/10.1088/0067-0049/209/2/19
  18. Cosmology with peculiar velocities: observational effects vol.463, pp.4, 2016, https://doi.org/10.1093/mnras/stw2252
  19. Edgeworth streaming model for redshift space distortions vol.92, pp.6, 2015, https://doi.org/10.1103/PhysRevD.92.063004
  20. Using the topology of large-scale structure in the WiggleZ Dark Energy Survey as a cosmological standard ruler vol.437, pp.3, 2014, https://doi.org/10.1093/mnras/stt2062
  21. Galaxy and mass assembly: Redshift space distortions from the clipped galaxy field vol.93, pp.2, 2016, https://doi.org/10.1103/PhysRevD.93.023525
  22. Halo shapes, initial shear field, and cosmic web vol.484, 2014, https://doi.org/10.1088/1742-6596/484/1/012049
  23. Numerical simulations of the dark universe: State of the art and the next decade vol.1, pp.1-2, 2012, https://doi.org/10.1016/j.dark.2012.10.002
  24. HECTOMAP AND HORIZON RUN 4: DENSE STRUCTURES AND VOIDS IN THE REAL AND SIMULATED UNIVERSE vol.818, pp.2, 2016, https://doi.org/10.3847/0004-637X/818/2/173
  25. THE CHALLENGE OF THE LARGEST STRUCTURES IN THE UNIVERSE TO COSMOLOGY vol.759, pp.1, 2012, https://doi.org/10.1088/2041-8205/759/1/L7
  26. Systematic treatment of non-linear effects in Baryon Acoustic Oscillations vol.125, 2016, https://doi.org/10.1051/epjconf/201612503006
  27. The Copernicus Complexio: a high-resolution view of the small-scale Universe vol.457, pp.4, 2016, https://doi.org/10.1093/mnras/stw214
  28. SEMI-ANALYTIC GALAXY EVOLUTION (SAGE): MODEL CALIBRATION AND BASIC RESULTS vol.222, pp.2, 2016, https://doi.org/10.3847/0067-0049/222/2/22
  29. HORIZON RUN 3: TOPOLOGY AS A STANDARD RULER vol.799, pp.2, 2015, https://doi.org/10.1088/0004-637X/799/2/176
  30. Cosmological constraints from the redshift dependence of the Alcock–Paczynski test and volume effect: galaxy two-point correlation function vol.450, pp.1, 2015, https://doi.org/10.1093/mnras/stv622
  31. ON PHYSICAL SCALES OF DARK MATTER HALOS vol.792, pp.2, 2014, https://doi.org/10.1088/0004-637X/792/2/124
  32. Effective description of dark matter as a viscous fluid vol.125, 2016, https://doi.org/10.1051/epjconf/201612503018
  33. Cosmological perturbation theory at three-loop order vol.2014, pp.01, 2014, https://doi.org/10.1088/1475-7516/2014/01/010
  34. A giant ring-like structure at 0.78 < z < 0.86 displayed by GRBs vol.452, pp.3, 2015, https://doi.org/10.1093/mnras/stv1421
  35. Gaussian streaming with the truncated Zel’dovich approximation vol.94, pp.12, 2016, https://doi.org/10.1103/PhysRevD.94.123522
  36. New Fitting Formula for Cosmic Nonlinear Density Distribution vol.843, pp.1, 2017, https://doi.org/10.3847/1538-4357/aa74b9
  37. EUNHA: A NEW COSMOLOGICAL HYDRODYNAMIC SIMULATION CODE vol.47, pp.3, 2014, https://doi.org/10.5303/JKAS.2014.47.3.87
  38. Cosmological post-Newtonian equations from nonlinear perturbation theory vol.2013, pp.08, 2013, https://doi.org/10.1088/1475-7516/2013/08/040
  39. Hunting down systematics in baryon acoustic oscillations after cosmic high noon vol.458, pp.1, 2016, https://doi.org/10.1093/mnras/stw312
  40. ICE-COLA: towards fast and accurate synthetic galaxy catalogues optimizing a quasi-N-body method vol.459, pp.3, 2016, https://doi.org/10.1093/mnras/stw797
  41. Quantifying the Cosmic Web using the Shapefinder diagonistic vol.11, pp.S308, 2014, https://doi.org/10.1017/S1743921316009960
  42. ASYMMETRIC ABSORPTION PROFILES OF Lyα AND Lyβ IN DAMPED Lyα SYSTEMS vol.772, pp.2, 2013, https://doi.org/10.1088/0004-637X/772/2/123
  43. N-body methods for relativistic cosmology vol.31, pp.23, 2014, https://doi.org/10.1088/0264-9381/31/23/234006
  44. Large scale structure from viscous dark matter vol.2015, pp.11, 2015, https://doi.org/10.1088/1475-7516/2015/11/049
  45. Scaling relations for galaxy clusters in the Millennium-XXL simulation vol.426, pp.3, 2012, https://doi.org/10.1111/j.1365-2966.2012.21830.x
  46. Cosmological Parameter Estimation Using the Genus Amplitude—Application to Mock Galaxy Catalogs vol.853, pp.1, 2018, https://doi.org/10.3847/1538-4357/aaa24f
  47. Resolution convergence in cosmological hydrodynamical simulations using adaptive mesh refinement vol.477, pp.1, 2018, https://doi.org/10.1093/mnras/sty673