Context. When constructing galaxy mock catalogs based on suites of dark matter halo catalogs generated with approximated, calibrated or machine-learning approaches, the assignment of intrinsic properties for such tracers is a step of
paramount relevance, given that these can shape the abundance and spatial distribution of mock galaxies and galaxy clusters.
Aims. We explore the possibility to assign properties of dark matter halos within the context of calibrated/learning approaches, explicitly using clustering information. The goal is to retrieve the correct signal of primary and secondary large-effective bias as a function of properties reconstructed solely based on phase-space properties of the halo distribution and dark matter density field.
Methods. The algorithm reconstructs a set halo properties (such as virial mass, maximum circular velocity, concentration
and spin) constraint to reproduce both primary and secondary (or assembly) bias. The key ingredients of the algorithm
are the implementation of individually-assigned large-scale effective bias, a multi-scale approach to account for halo exclusion and a hierarchical assignment of halo properties.
Results. The method facilitates the assignment of halo properties aiming at replicating the large-scale effective bias,both primary and secondary. This improves over previous methods in the literature, especially at the high-mass end population.
Conclusions. We have designed an strategy to reconstruct the main properties of dark matter halos obtained by
calibrated/learning algorithms, such that the one and two-point statistics (on large scales) replicates the signal from
detailed N-body simulations. We encourage the application of this strategy (or the implementation of our algorithm)
for the generation of mock catalogs of dark matter halos based on approximated methods.