"Angular Galaxy Clustering from Deep Surveys with Complex Selection Effects"

Photometric galaxy surveys provide rich information on the Universe’s large-scale structure, but complex selection effects can bias angular two-point correlation function (2PCF) measurements. In this seminar, Ziang Yan presents results from the newly published KiDS-Legacy sample, highlighting how seeing variations and other selection effects can overestimate the 2PCF.

The talk introduces a machine learning-based approach — combining self-organising maps (SOMs) and hierarchical clustering (HC) — to recover an organised random (OR) distribution matching the galaxy sample’s selection pattern. Validated on realistic mock data, this method removes significant bias in cosmological parameter estimates. Application to real KiDS-Legacy data demonstrates the robustness of the corrected 2PCF.

Hybrid format: in-person at the USM Lecture Hall and online via Zoom