Learning Robust Models for Multi-Source Gravitational-Wave Analysis
Foreground modeling for calibrated noise reconstruction and source inference
Abstract
Unresolved Galactic binaries complicate both instrumental-noise estimation and source inference in space-based data. This talk explores a learned aggregate foreground model that can be reused for noise characterization and foreground-aware Bayesian inference, without first resolving every binary into an individual catalog entry.
Date
Jul 14, 2026 2:00 PM — 2:30 PM
Location
Coriander Room, Shaw Foundation Alumni House, National University of Singapore
Singapore,
