Spectral Properties of Anomalous Microwave Emission in 144 Galactic Clouds
Roke Cepeda-Arroita, J. A. Rubiño-Martín, R. T. Génova-Santos, C. Dickinson, S. E. Harper, F. Poidevin, M. W. Peel, R. Rebolo, D. Adak, A. Almeida, K. Aryan, R. B. Barreiro, F. J. Casas, J. M. Casas, J. Chluba, M. Fernández-Torreiro, D. Herranz, G. A. Hoerning, Michael E. Jones, J. Leech, E. Martínez-González, T. J. Pearson, Angela C. Taylor, P. Vielva, R. A. Watson, Z. Zhang
公開日: 2025/10/6
Abstract
Anomalous Microwave Emission (AME) is a diffuse microwave component thought to arise from spinning dust grains, yet remains poorly understood. We analyze AME in 144 Galactic clouds by combining low-frequency maps from S-PASS (2.3 GHz), C-BASS (4.76 GHz), and QUIJOTE (10-20 GHz) with 21 ancillary maps. Using aperture photometry and parametric SED fitting via MCMC methods without informative priors, we measure AME emissivity, peak frequency, and spectral width. We achieve peak frequency constraints nearly three times tighter than previous work and identify 83 new AME sources. AME spectra are generally broader than predicted by spinning dust models for a single phase of the interstellar medium, suggesting either multiple spinning dust components along the line of sight or incomplete representation of the grain size distribution in current models. However, the narrowest observed widths match theoretical predictions, supporting the spinning dust hypothesis. The AME amplitude correlates most strongly with the thermal dust peak flux and radiance, showing $\sim30$% scatter and sublinear scaling, which suggests reduced AME efficiency in regions with brighter thermal dust emission. AME peak frequency increases with thermal dust temperature in a trend current theoretical models do not reproduce, indicating that spinning dust models must incorporate dust evolution and radiative transfer in a self-consistent framework where environmental parameters and grain properties are interdependent. PAH tracers correlate with AME emissivity, supporting a physical link to small dust grains. Finally, a log-Gaussian function provides a good empirical description of the AME spectrum across the sample, given current data quality and frequency coverage.