SpyDust: an improved and extended implementation for modeling spinning dust radiation

TL;DR

SpyDust is an enhanced Python-based model for spinning dust emission that accounts for diverse grain shapes, updates physical effects, and analyzes parameter degeneracies to improve understanding of interstellar dust radiation.

Contribution

It introduces a comprehensive extension of the spinning dust model considering various grain shapes and updates physical effects, along with a detailed analysis of parameter degeneracies.

Findings

Shape-dependent emission effects identified.

Strong correlations among model parameters revealed.

Most SED variations captured by four principal components.

Abstract

This paper presents 'SpyDust', an improved and extended implementation of the spinning dust emission model based on a Fokker-Planck treatment. 'SpyDust' serves not only as a Python successor to 'spdust', but also incorporates some corrections and extensions. Unlike 'spdust', which is focused on specific grain shapes, 'SpyDust' considers a wider range of grain shapes and provides the corresponding grain dynamics, directional radiation field and angular momentum transports. We recognise the unique effects of different grain shapes on emission, in particular the shape-dependent mapping between rotational frequency and spectral frequency. In addition, we update the expressions for effects of electrical dipole radiation back-reaction and plasma drag on angular momentum dissipation. We also discuss the degeneracies in describing the shape of the spectral energy distribution (SED) of spinning dust grains with the interstellar environmental parameters. Using a typical Cold Neutral Medium (CNM) environment as an example, we perform a perturbative analysis of the model parameters, revealing strong positive or negative correlations between them. A principal component analysis (PCA) shows that four dominant modes can linearly capture most of the SED variations, highlighting the degeneracy in the parameter space of the SED shape in the vicinity of the chosen CNM environment. This opens the possibility for future applications of moment expansion methods to reduce the dimensionality of the encountered SED parameter space.