Improved Cosmological Constraints from Morphology-Based Marked Correlation Functions

The cosmic web contains morphology-dependent information that is not fully captured by standard two-point statistics. We construct morphology-based marked correlation functions (MCFs) by assigning marks to halos according to the cosmic-web morphology identified with the $\text{textsc}Nexus$ algorithm. Using the $\text{textsc}Kun$ simulation suite, which spans 129 $w_0{w}_a$CDM cosmologies, we build Gaussian-process emulators for the MCFs as functions of cosmological parameters and tracer bias. We then apply the emulators to mock halo catalogues from the independent $\text{textsc}Jiutian$ simulation and perform a joint likelihood analysis to quantify the resulting cosmological constraints. We consider two marker choices: a discrete morphology marker and a continuous morphology strength marker. The continuous marker improves the Figure of Merit (FoM) by a factor of $\sim 8.6$ relative to the standard 2PCF and reduces the $1\sigma$ uncertainty on ${\sigma }_8$ by a factor of $\sim 5$. The discrete marker gives a more modest FoM improvement of $\sim 17\mathrm{\%}$. We further test the impact of tracer selection by varying the halo mass threshold by a factor of $\sim 4.5$. Even for the lowest mass threshold, the continuous marker remains unbiased and achieves a FoM about $\sim 3.4$ times higher than that of the 2PCF alone. These results show that morphology-based MCFs, combined with simulation-based emulation, provide a useful framework for extracting additional cosmological information from large-scale structure surveys.