Spectral Signatures of Spinning Dust from Grain Ensembles in Diverse Environments: A Combined Theoretical and Observational Study

TL;DR

This study combines theoretical modeling and observational data to understand how dust grain properties and environmental factors influence the spectral features of anomalous microwave emission, highlighting the role of grain size, shape, and phase-dependent parameters.

Contribution

It introduces a comprehensive analysis of dust grain ensemble effects on AME spectra, emphasizing the importance of environmental and grain property variations, and proposes new methods for spectral analysis.

Findings

AME spectral features are mainly controlled by grain size, shape, and environmental phase.

Molecular Clouds are consistent with models; Dark Clouds show minor deviations.

HII regions exhibit significant spectral offsets and grain depletion effects.

Abstract

Recent observations of anomalous microwave emission (AME) reveal spectral features that are not readily reproduced by spinning dust models. We examine how dust grain distributions and environmental parameters determine the peak frequency and spectral width of AME spectral energy distribution (SED). Using Monte Carlo sampling and global sensitivity analysis, we find that AME features are dominantly controlled by three parameters: grain size, shape, and a phase-dependent environmental parameter. We also quantify the effects of SED broadening from ensembles of these dominant parameters, finding that the level of tension with observations is strongly phase dependent: Molecular Cloud (MC) is fully consistent, Dark Cloud (DC) shows minor deviations, and HII regions exhibit significant offsets in peak frequency. The discrepancy in HII echoes the observed depletion of small dust grains, particularly polycyclic aromatic hydrocarbons (PAHs), in HII regions. However, an observational HII region may still have AME originating from nearby non-HII clouds. In this case, model calculations for HII regions would be inappropriate. Reproducing MC and DC AME features requires ensemble variations in both grain size and environmental parameters are required to reproduce the observed spread in peak frequency and spectral width. We further propose moment expansion and emulation-based inference methods for future AME spectral analysis.