Expositor: Dante Javier Paz (OAC-UNC / IATE-CONICET)
Fecha: viernes 09 de octubre, 11:30 hs.
Resumen: The cross-correlation between cosmic voids and tracer galaxies is a powerful probe for testing cosmology via redshift-space distortions and the Alcock-Paczyński effect.
However, performing this analysis in a fully observational redshift-space configuration presents severe theoretical challenges. The standard modelling framework fails due to a selection bias by artificially promoting spurious and intrinsically anisotropic voids into the sample. This contamination breaks the fundamental assumption of spherical symmetry and generates a massive, non-dynamical quadrupole. In this talk, we present a comprehensive framework to overcome these challenges. On the one hand, we introduce a sample-cleaning procedure that utilises density-field reconstruction strictly as a pre-processing step to isolate and remove the spurious voids, yielding an unbiased redshift-space sample for the subsequent analysis. On the other hand, we introduce an extension of the Gaussian Streaming Model capable of precisely describing this clean configuration. By shifting to void-frame kinematics, our model incorporates two critical variables: the line-of-sight off-centring driven by void bulk velocities, and the previously neglected plane-of-sky off-centring sourced by surrounding environmental anisotropies. Through extensive Monte Carlo testing, we demonstrate that the variance of these spatial shifts fundamentally alters the redshift-space quadrupole. Building on these Monte Carlo results, we derive an analytical extension of the Gaussian Streaming Model. Our extended model successfully recovers the observed multipoles and is shown to be highly universal, remaining robust across varying tracer mass cuts, redshifts and distinct void-finding definitions. This framework provides a complete theoretical foundation for future full redshift-space void analyses.
