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

被引：18

作者：

Pelgrims, V. ^{[1
,2
,3
]}

Panopoulou, G. V. ^{[4
]}

Tassis, K. ^{[1
,2
,3
]}

Pavlidou, V. ^{[1
,2
,3
]}

Basyrov, A. ^{[5
]}

Blinov, D. ^{[1
,2
,3
]}

Gjerlow, E. ^{[5
]}

Kiehlmann, S. ^{[1
,2
,3
]}

Mandarakas, N. ^{[1
,2
,3
]}

Papadaki, A. ^{[1
,2
,3
,6
]}

Skalidis, R. ^{[1
,2
,3
]}

Tsouros, A. ^{[1
,2
,3
]}

Anche, R. M. ^{[7
,8
]}

Eriksen, H. K. ^{[5
]}

Ghosh, T. ^{[9
]}

Kypriotakis, J. A. ^{[1
,2
,3
]}

Maharana, S. ^{[1
,2
,3
,8
]}

Ntormousi, E. ^{[1
,2
,3
,10
]}

Pearson, T. J. ^{[11
]}

Potter, S. B. ^{[12
]}

Ramaprakash, A. N. ^{[1
,8
,11
]}

Readhead, A. C. S. ^{[11
]}

Wehus, I. K. ^{[5
]}

机构：

[1] Fdn Res & Technol Hellas, Inst Astrophys, N Plastira 100, Iraklion 71110, Greece

[2] Univ Crete, Dept Phys, Voutes Univ Campus, Iraklion 70013, Greece

[3] Univ Crete, Inst Theoret & Computat Phys, Voutes Univ Campus, Iraklion 70013, Greece

[4] CALTECH, MC350-17,1200 East Calif Blvd, Pasadena, CA 91125 USA

[5] Univ Oslo, Inst Theoret Astrophys, POB 1029, N-0315 Oslo, Norway

[6] Fdn Res & Technol Hellas, Inst Comp Sci, Iraklion 71110, Greece

[7] Steward Observ, Dept Astron, Tucson, AZ 85721 USA

[8] Interuniv Ctr Astron & Astrophys, Post Bag 4, Pune 411007, India

[9] HBNI, Natl Inst Sci Educ & Res, Sch Phys Sci, Jatni 752050, Orissa, India

[10] South African Astron Observ, POB 9, ZA-7935 Cape Town, South Africa

[11] CALTECH, Cahill Ctr Astron & Astrophys, 1216 E Calif Blvd, Pasadena, CA 91125 USA

[12] Univ Johannesburg, Dept Phys, POB 524, ZA-2006 Auckland Pk, South Africa

来源：

ASTRONOMY & ASTROPHYSICS | 2023年 / 670卷

基金：

欧洲研究理事会; 美国国家科学基金会; 新加坡国家研究基金会;

关键词：

dust; extinction; ISM:magnetic fields; ISM:structure; polarization; methods:statistical; MOLECULAR CLOUDS; INTERSTELLAR DUST; OBSERVATIONAL CONSTRAINTS; FIELD MORPHOLOGY; STAR-FORMATION; LOCAL BUBBLE; GAIA; TURBULENCE; EMISSION; MODEL;

D O I：

10.1051/0004-6361/202244625

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

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 similar to 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.

引用

页数：30