We investigate the feasibility of using COmoving Lagrangian Acceleration
(COLA) technique to efficiently generate galaxy mock catalogues that can
accurately reproduce the statistical properties of observed galaxies. Our
proposed scheme combines the subhalo abundance matching (SHAM) procedure with
COLA simulations, utilizing only three free parameters: the scatter magnitude
(Οscatβ) in SHAM, the initial redshift (zinitβ) 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 zβ[0.45,0.55]. We perform GADGET simulation
and low-resolution COLA simulations with various combinations of (zinitβ,da), each using 10243 particles in an 800Β hβ1Mpc 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
Οscatβ. We have found that by setting zinitβ=29 and
da=1/30, we achieve a good agreement between COLA mock and CMASS NGC galaxies
within the range of 4 to 20Β hβ1Mpc, with a computational cost two
orders of magnitude lower than that of the 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 similar performance between GADGET mock and COLA mock catalogues with the
CMASS NGC galaxies. Furthermore, we assess the robustness of the COLA mock
catalogues across different cosmological models, demonstrating consistent
results in the resulting 2PCFs. Our findings suggest that COLA simulations are
a promising tool for efficiently generating mock catalogues for emulators and
machine learning analyses in exploring the large-scale structure of the
Universe.Comment: 24 pages, 14 figures, 4 table