  The increase in the observed volume in cosmological surveys imposes various
challenges on simulation preparations. Firstly, the volume of the simulations
required increases proportionally to the observations. However, large-volume
simulations are quickly becoming computationally intractable. Secondly,
on-going and future large-volume survey are targeting smaller objects, e.g.
emission line galaxies, compared to the earlier focus, i.e. luminous red
galaxies. They require the simulations to have higher mass resolutions. In this
work we present a machine learning (ML) approach to calibrate the halo
catalogue of a low-resolution (LR) simulation by training with a paired
high-resolution (HR) simulation with the same background white noise, thus we
can build the training data by matching HR haloes to LR haloes in a one-to-one
fashion. After training, the calibrated LR halo catalogue reproduces the
mass-clustering relation for mass down to $2.5\times 10^{11}~h^{-1}M_\odot$
within $5~{\rm per~cent}$ at scales $k<1~h\,\rm Mpc^{-1}$. We validate the
performance of different statistics including halo mass function, power
spectrum, two-point correlation function, and bispectrum in both real and
redshift space. Our approach generates high-resolution-like halo catalogues
($>200$ particles per halo) from low-resolution catalogues ($>25$ particles per
halo) containing corrected halo masses for each object. This allows to bypass
the computational burden of a large-volume real high-resolution simulation
without much compromise in the mass resolution of the result. The cost of our
ML approach ($\sim 1$ CPU-hour) is negligible compared to the cost of a
$N$-body simulation (e.g. millions of CPU-hours), The required computing time
is cut a factor of 8.
