The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: N-body mock challenge for the eBOSS emission line galaxy sample
Shadab Alam, Arnaud de Mattia, Amélie Tamone, S. Àvila, J. A. Peacock, Violeta González-Pérez, A. G. Smith, Anand Raichoor, Ashley J. Ross, Julian Bautista, E. Burtin, Johan Comparat, Kyle Dawson, Hélion du Mas des Bourboux, S. Escoffier, Héctor Gil-Marín, Salman Habib, Katrin Heitmann, Jiamin Hou, Faizan G Mohammad, Eva-Maria Mueller, Richard Neveux, Romain Paviot, Will J. Percival, Graziano Rossi, V. Ruhlmann-Kleider, Rita Tojeiro, M. Vargas-Magaña, Cheng Zhao, Gong‐Bo Zhao
Abstract
ABSTRACT Cosmological growth can be measured in the redshift space clustering of galaxies targeted by spectroscopic surveys. Accurate prediction of clustering of galaxies will require understanding galaxy physics, which is a very hard and highly non-linear problem. Approximate models of redshift space distortion (RSD) take a perturbative approach to solve the evolution of dark matter and galaxies in the universe. In this paper, we focus on extended Baryon Oscillation Spectroscopic (eBOSS) emission line galaxies (ELGs) that live in intermediate mass haloes. We create a series of mock catalogues using haloes from the Multidark and outer rim dark matter only N-body simulations. Our mock catalogues include various effects inspired by baryonic physics such as assembly bias and the characteristics of satellite galaxies kinematics, dynamics, and statistics deviating from dark matter particles. We analyse these mocks using the TNS RSD model in Fourier space and the convolution Lagrangian perturbation theory (CLPT) in configuration space. We conclude that these two RSD models provide an unbiased measurement of RSD within the statistical error of our mocks. We obtain the conservative theoretical systematic uncertainty of $3.3{{\ \rm per\ cent}}$, $1.8{{\ \rm per\ cent}}$, and $1.5{{\ \rm per\ cent}}$ in fσ8, α∥, and α⊥, respectively, for the TNS and CLPT models. We note that the estimated theoretical systematic error is an order of magnitude smaller than the statistical error of the eBOSS ELG sample and hence are negligible for the purpose of the current eBOSS ELG analysis.