Speaker
Description
Anomalous Microwave Emission (AME) remains a critical uncertainty in global sky modeling, with its widely hypothesized origin tied to spinning Polycyclic Aromatic Hydrocarbons (PAHs). Robustly separating AME from other diffuse Galactic foregrounds requires using multi-frequency data across the electromagnetic spectrum. In this work, we present a blind component separation approach using Principal Component Analysis (PCA) applied to a comprehensive dataset spanning radio to infrared wavelengths. Our dataset synthesizes observations from space-based missions (Planck, WMAP, DIRBE, Haslam) and incorporates critical ground-based surveys (e.g., C-BASS, QUIJOTE). To physically interpret the extracted PCA components, we introduce a novel framework bridging blind and parametric techniques. We construct a theoretical basis via a moment expansion of standard foreground Spectral Energy Distributions (SEDs). By orthonormalizing this model basis, we can predict the uncorrelated spatial maps expected from the underlying physics. If the parametric model accurately describes the sky, its basis will span the exact same subspace as the data-derived PCA eigenvectors. We have successfully validated this methodology on simulated skies, demonstrating a high spatial correlation between the model-predicted components and the blind PCA maps. Building on this success, we will present the application of our pipeline to observational datasets, with the ultimate goal of isolating the AME component and robustly testing its correlation with infrared PAH tracers.