Demonstration of Magnetic Field Tomography with Starlight Polarization toward a Diffuse Sightline of the ISM
Abstract
The availability of large data sets with stellar distance and polarization information will enable a tomographic reconstruction of the (plane-of-the-sky-projected) interstellar magnetic field in the near future. We demonstrate the feasibility of such a decomposition within a small region of the diffuse interstellar medium (ISM). We combine measurements of starlight (R-band) linear polarization obtained using the RoboPol polarimeter with stellar distances from the second Gaia data release. The stellar sample is brighter than 17 mag in the R-band and reaches out to several kiloparsecs from the Sun. H i emission spectra reveal the existence of two distinct clouds along the line of sight. We decompose the line-of-sight-integrated stellar polarizations to obtain the mean polarization properties of the two clouds. The two clouds exhibit significant differences in terms of column density and polarization properties. Their mean plane-of-the-sky magnetic field orientation differs by 60°. We show how our tomographic decomposition can be used to constrain our estimates of the polarizing efficiency of the clouds as well as the frequency dependence of the polarization angle of polarized dust emission. We also demonstrate a new method to constrain cloud distances based on this decomposition. Our results represent a preview of the wealth of information that can be obtained from a tomographic map of the ISM magnetic field.
Additional Information
© 2019 The American Astronomical Society. Received 2018 October 22; revised 2018 December 31; accepted 2019 January 8; published 2019 February 11. We thank A. Brimis, I. Komis, I. Leonidaki, and N. Mandarakas for their help during the observations and T. Ghosh for helping with the analysis of Planck data. We thank N. Kylafis for useful suggestions on the paper. We thank the anonymous reviewer for their helpful comments. A.N.R., G.V.P., and A.C.S.R. acknowledge support from the National Science Foundation, under grant number AST-1611547. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 771282. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC; https://www.cosmos.esa.int/web/gaia/dpac/consortium). This research made use of the following Python packages: APLpy (Robitaille & Bressert 2012), Astropy (Astropy Collaboration et al. 2013), and healpy (Górski et al. 2005). Facilities: Skinakas:1.3 m - , Gaia - , Effelsberg - , CDS - , IRSA - , Planck - . Software: Astropy, aplpy, healpy.Attached Files
Published - Panopoulou_2019_ApJ_872_56.pdf
Submitted - 1809.09804.pdf
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Additional details
- Eprint ID
- 92852
- Resolver ID
- CaltechAUTHORS:20190212-150402789
- NSF
- AST-1611547
- European Research Council (ERC)
- 771282
- Created
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2019-02-13Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Astronomy Department