Fast Generation of Mock Galaxy Catalogs with COLA

Abstract

Abstract We investigate the feasibility of using the comoving Lagrangian acceleration (COLA) technique to efficiently generate galaxy mock catalogs that can accurately reproduce the statistical properties of observed galaxies. Our proposed scheme combines the subhalo abundance-matching (SHAM) procedure with COLA simulations, using only three free parameters: the scatter magnitude (σ scat) in SHAM, the initial redshift (z init) of the COLA simulation, and the time stride (da) used by COLA. In this proof-of-concept study, we focus on a subset of BOSS CMASS NGC galaxies within the redshift range z ∈ [0.45, 0.55]. We perform GADGET simulation and low-resolution COLA simulations with various combinations of (z init, da), each using 10243 particles in an 800 h −1 Mpc box. By minimizing the difference between COLA mock and CMASS NGC galaxies for the monopole of the two-point correlation function (2PCF), we obtain the optimal σ scat. We have found that by setting z init = 29 and da = 1/30, we achieve a good agreement between COLA mock and CMASS NGC galaxies within the range of 4–20 h −1 Mpc, with a computational cost lower by 2 orders of magnitude than that of the GADGET N-body code. Moreover, a detailed verification is performed by comparing various statistical properties, such as anisotropic 2PCF, three-point clustering, and power spectrum multipoles, which shows a similar performance of the GADGET mock and COLA mock catalogs with the CMASS NGC galaxies. Furthermore, we assess the robustness of the COLA mock catalogs for different cosmological models, demonstrating consistent results in the resulting 2PCFs. Our findings suggest that COLA simulations are a promising tool for efficiently generating mock catalogs for emulators and machine-learning analyses to explore the large-scale structure of the Universe.