Abstract

Context. Recent efforts to identify secondary variations in the halo occupation distribution (HOD) have primarily focused on simulations that examine the role of large-scale cosmic environments, such as superclusters, filaments, and under-dense regions or voids. If present, these variations can yield valuable insights into galaxy formation mechanisms, halo assembly processes, and the influence of external factors on the evolution of cosmic structure. Aims. We aim to explore whether the secondary trends in the HOD driven by the large-scale structure of the Universe are present observationally. In particular, we examined whether the HOD depends on the distance to key features of the cosmic web by explicitly quantifying these spatial relationships. We further analyzed whether HODs vary across different cosmic environments, as defined by critical point classifications, and assessed the influence of intrinsic galaxy properties, such as the central galaxy color. Methods. We created volume-limited galaxy samples from SDSS DR18 and used a group catalog to determine halo masses and to identify central and satellite galaxy membership. Additionally, we employed a DisPerSE catalog to locate critical points such as maxima, minima, and filaments in the cosmic web. We assessed how the HOD varies based on proximity to these features and analyzed these variations across five distinct cosmic environments. Furthermore, we investigated trends related to the color of central galaxies and tested the reliability of our results by using alternative DisPerSE catalogs generated with different smoothing scales and persistence thresholds. Results. Our analysis confirms that the large-scale cosmic environment only weakly influences the HOD. However, second-order environmental dependences might be revealed through a multivariate approach that combines both local and large-scale environment metrics with intrinsic galaxy properties. Future investigations that employ next-generation surveys with improved statistical power, coupled with sophisticated modeling techniques, may provide the necessary precision to detect and characterize these subtle environmental correlations.