Starlight-polarization-based tomography of the magnetized ISM: PASIPHAE’s line-of-sight inversion method

¹ Institute of Astrophysics, Foundation for Research and Technology-Hellas, N. Plastira 100, Vassilika Vouton, 71110 Heraklion, Greece
² Department of Physics, and Institute for Theoretical and Computational Physics, University of Crete, Voutes University campus, 70013 Heraklion, Greece
e-mail: pelgrims@physics.uoc.gr
³ Hubble Fellow, California Institute of Technology, MC350-17, 1200 East California Boulevard, Pasadena, CA 91125, USA
⁴ Institute of Theoretical Astrophysics, University of Oslo, PO Box 1029 Blindern, 0315 Oslo, Norway
⁵ Institute of Computer Science, Foundation for Research and Technology-Hellas, 71110 Heraklion, Greece
⁶ Department of Astronomy/Steward Observatory, Tucson, AZ 85721-0065, USA
⁷ Inter-University Centre for Astronomy and Astrophysics, Post bag 4, Ganeshkhind, Pune 411007, India
⁸ School of Physical Sciences, National Institute of Science Education and Research, HBNI, Jatni 752050, Odisha, India
⁹ Scuola Normale Superiore di Pisa, piazza dei Cavalieri 7, 56126 Pisa, Italy
¹⁰ Cahill Center for Astronomy and Astrophysics, California Institute of Technology, 1216 E California Blvd, Pasadena, CA 91125, USA
¹¹ Department of Physics, University of Johannesburg, PO Box 524, Auckland Park 2006, South Africa
¹² South African Astronomical Observatory, PO Box 9, Observatory, 7935 Cape Town, South Africa

Received: 29 July 2022
Accepted: 28 December 2022

Abstract

We present the first Bayesian method for tomographic decomposition of the plane-of-sky orientation of the magnetic field with the use of stellar polarimetry and distance. This standalone tomographic inversion method presents an important step forward in reconstructing the magnetized interstellar medium (ISM) in three dimensions within dusty regions. We develop a model in which the polarization signal from the magnetized and dusty ISM is described by thin layers at various distances, a working assumption which should be satisfied in small-angular circular apertures. Our modeling makes it possible to infer the mean polarization (amplitude and orientation) induced by individual dusty clouds and to account for the turbulence-induced scatter in a generic way. We present a likelihood function that explicitly accounts for uncertainties in polarization and parallax. We develop a framework for reconstructing the magnetized ISM through the maximization of the log-likelihood using a nested sampling method. We test our Bayesian inversion method on mock data, representative of the high Galactic latitude sky, taking into account realistic uncertainties from Gaia and as expected for the optical polarization survey PASIPHAE according to the currently planned observing strategy. We demonstrate that our method is effective at recovering the cloud properties as soon as the polarization induced by a cloud to its background stars is higher than ~0.1% for the adopted survey exposure time and level of systematic uncertainty. The larger the induced polarization is, the better the method’s performance, and the lower the number of required stars. Our method makes it possible to recover not only the mean polarization properties but also to characterize the intrinsic scatter, thus creating new ways to characterize ISM turbulence and the magnetic field strength. Finally, we apply our method to an existing data set of starlight polarization with known line-of-sight decomposition, demonstrating agreement with previous results and an improved quantification of uncertainties in cloud properties.