Faraday Tomography with Sparse Modeling. (arXiv:1811.10610v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Akiyama_K/0/1/0/all/0/1">Kazunori Akiyama</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Akahori_T/0/1/0/all/0/1">Takuya Akahori</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Miyashita_Y/0/1/0/all/0/1">Yoshimitsu Miyashita</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ideguchi_S/0/1/0/all/0/1">Shinsuke Ideguchi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Yamaguchi_R/0/1/0/all/0/1">Ryosuke Yamaguchi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ikeda_S/0/1/0/all/0/1">Shiro Ikeda</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Takahashi_K/0/1/0/all/0/1">Keitaro Takahashi</a>
Faraday tomography (or rotation measure synthesis) is a procedure to convert
linear polarization spectra into the Faraday dispersion function, which
provides us with unique information of magneto-ionic media along the line of
sight. Mathematical formulation of Faraday tomography is similar to
polarimetric imaging of radio interferometry, where many new methods have been
actively developed and shown to outperform the standard CLEAN approaches. In
this paper, we propose a sparse reconstruction technique to Faraday tomography.
This technique is being developed for interferometric imaging and utilizes
computationally less expensive convex regularization functions such as
$ell_1$-norm and total variation (TV) or total squared variation (TSV). The
proposed technique solves a convex optimization, and therefore its solution is
determined uniquely regardless of the initial condition for given
regularization parameters that can be optimized by data themselves. Using a
physically-motivated model of turbulent galactic magnetized plasma, we
demonstrate that the proposed technique outperforms RM-CLEAN and provides
higher-fidelity reconstruction. The proposed technique would be a powerful tool
in broadband polarimetry with the Square Kilometre Array (SKA) and its
precursors.
Faraday tomography (or rotation measure synthesis) is a procedure to convert
linear polarization spectra into the Faraday dispersion function, which
provides us with unique information of magneto-ionic media along the line of
sight. Mathematical formulation of Faraday tomography is similar to
polarimetric imaging of radio interferometry, where many new methods have been
actively developed and shown to outperform the standard CLEAN approaches. In
this paper, we propose a sparse reconstruction technique to Faraday tomography.
This technique is being developed for interferometric imaging and utilizes
computationally less expensive convex regularization functions such as
$ell_1$-norm and total variation (TV) or total squared variation (TSV). The
proposed technique solves a convex optimization, and therefore its solution is
determined uniquely regardless of the initial condition for given
regularization parameters that can be optimized by data themselves. Using a
physically-motivated model of turbulent galactic magnetized plasma, we
demonstrate that the proposed technique outperforms RM-CLEAN and provides
higher-fidelity reconstruction. The proposed technique would be a powerful tool
in broadband polarimetry with the Square Kilometre Array (SKA) and its
precursors.
http://arxiv.org/icons/sfx.gif
