Speaker
Description
Cosmic voids are sensitive probes of galaxy formation and evolution, but their identification in photometric surveys is complicated by redshift uncertainties. In this work, we simulate the observational limitations of the J-PAS photometric survey by modelling photometric redshift errors based on the J-PAS Internal Data Release using the FLAMINGO hydrodynamical simulation. We construct two galaxy mocks: an ideal galaxy mock catalogue (without redshift errors) and perturbed mock catalogue (with modelled redshift errors).
To identify voids robustly in the presence of redshift errors, we compute gravitational potential fields from each galaxy sample, which provide a physically motivated smoothing of small-scale density fluctuations, and apply a watershed-based void-finding algorithm. We compare void properties, including sizes, shapes, and density profiles, and assess void recovery by computing the intersection-over-union of void volumes between the ideal and perturbed samples. Finally, we explore how galaxy properties—such as color, stellar mass, star formation rate (SFR), and specific SFR (sSFR)—depend on their location within low-potential regions and their distance from void centers.