Starlight-polarization-based tomography of the magnetized ISM: Pasiphae’s line-of-sight inversion method. (arXiv:2208.02278v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Pelgrims_V/0/1/0/all/0/1">V. Pelgrims</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Panopoulou_G/0/1/0/all/0/1">G. V. Panopoulou</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Tassis_K/0/1/0/all/0/1">K. Tassis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pavlidou_V/0/1/0/all/0/1">V. Pavlidou</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Basyrov_A/0/1/0/all/0/1">A. Basyrov</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Blinov_D/0/1/0/all/0/1">D. Blinov</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gjerlow_E/0/1/0/all/0/1">E. Gjerløw</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kiehlmann_S/0/1/0/all/0/1">S. Kiehlmann</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mandarakas_N/0/1/0/all/0/1">N. Mandarakas</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Papadaki_A/0/1/0/all/0/1">A. Papadaki</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Skalidis_R/0/1/0/all/0/1">R. Skalidis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Tsouros_A/0/1/0/all/0/1">A. Tsouros</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Anche_R/0/1/0/all/0/1">R. M. Anche</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Eriksen_H/0/1/0/all/0/1">H. K. Eriksen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ghosh_T/0/1/0/all/0/1">T. Ghosh</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kypriotakis_J/0/1/0/all/0/1">J. A. Kypriotakis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Maharana_S/0/1/0/all/0/1">S. Maharana</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ntormousi_E/0/1/0/all/0/1">E. Ntormousi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pearson_T/0/1/0/all/0/1">T. J. Pearson</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Potter_S/0/1/0/all/0/1">S. B. Potter</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ramaprakash_A/0/1/0/all/0/1">A. N. Ramaprakash</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Readhead_A/0/1/0/all/0/1">A. C. S. Readhead</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wehus_I/0/1/0/all/0/1">I. K. Wehus</a>
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 3D 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. 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 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 in recovering the cloud properties as soon as the
polarization induced by a cloud to its background stars is higher than $sim
0.1%$, for the adopted survey exposure time and level of systematic
uncertainty. Our method makes it possible to recover not only the mean
polarization properties but also to characterize the intrinsic scatter, thus
opening ways to characterize ISM turbulence and the magnetic field strength.
Finally, we apply our method to an existing dataset of starlight polarization
with known line-of-sight decomposition, demonstrating agreement with previous
results and an improved quantification of uncertainties in cloud properties.
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 3D 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. 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 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 in recovering the cloud properties as soon as the
polarization induced by a cloud to its background stars is higher than $sim
0.1%$, for the adopted survey exposure time and level of systematic
uncertainty. Our method makes it possible to recover not only the mean
polarization properties but also to characterize the intrinsic scatter, thus
opening ways to characterize ISM turbulence and the magnetic field strength.
Finally, we apply our method to an existing dataset of starlight polarization
with known line-of-sight decomposition, demonstrating agreement with previous
results and an improved quantification of uncertainties in cloud properties.
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