Radiative transfer with POLARIS

I. Analysis of magnetic fields through synthetic dust continuum polarization measurements

¹ Institut für Theoretische Physik und Astrophysik, Christian-Albrechts-Universität zu Kiel, Leibnizstraße 15, 24098 Kiel, Germany
e-mail: rbrauer@astrophysik.uni-kiel.de; wolf@astrophysik.uni-kiel.de
² Universität Heidelberg, Zentrum für Astronomie, Institut für Theoretische Astrophysik, Albert-Ueberle-Straße 2, 69120 Heidelberg, Germany
e-mail: reissl@uni-heidelberg.de

Received: 5 September 2014
Accepted: 17 March 2016

Abstract

Aims. We present POLARIS (POLArized RadIation Simulator), a newly developed three-dimensional Monte-Carlo radiative transfer code. POLARIS was designed to calculate dust temperature, polarization maps, and spectral energy distributions. It is optimized to handle data that results from sophisticated magneto-hydrodynamic simulations. The main purpose of the code is to prepare and analyze multi-wavelength continuum polarization measurements in the context of magnetic field studies in the interstellar medium. An exemplary application is the investigation of the role of magnetic fields in star formation processes.

Methods. We combine currently discussed state-of-the-art grain alignment theories with existing dust heating and polarization algorithms. We test the POLARIS code on multiple scales in complex astrophysical systems that are associated with different stages of star formation. POLARIS uses the full spectrum of dust polarization mechanisms to trace the underlying magnetic field morphology.

Results. Resulting temperature distributions are consistent with the density and position of radiation sources resulting from magneto-hydrodynamic (MHD) – collapse simulations. The calculated layers of aligned dust grains in the considered cirumstellar disk models are in excellent agreement with theoretical predictions. Finally, we compute unique patterns in synthetic multi-wavelength polarization maps that are dependent on applied dust-model and grain-alignment theory in analytical cloud models.