| 1. |
- Pavlidou, Vasiliki, et al.
(författare)
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The RoboPol optical polarization survey of gamma-ray-loud blazars
- 2016
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Ingår i: Monthly Notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 463:3
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Tidskriftsartikel (refereegranskat)abstract
- We present first results from RoboPol, a novel-design optical polarimeter operating at the Skinakas Observatory in Crete. The data, taken during the 2013 May-June commissioning of the instrument, constitute a single-epoch linear polarization survey of a sample of gamma-ray-loud blazars, defined according to unbiased and objective selection criteria, easily reproducible in simulations, as well as a comparison sample of, otherwise similar, gamma-ray-quiet blazars. As such, the results of this survey are appropriate for both phenomenological population studies and for tests of theoretical population models. We have measured polarization fractions as low as 0.015 down to R-mag of 17 and as low as 0.035 down to 18 mag. The hypothesis that the polarization fractions of gamma-ray-loud and gamma-ray-quiet blazars are drawn from the same distribution is rejected at the 3σ level. We therefore conclude that gamma-ray-loud and gamma-ray-quiet sources have different optical polarization properties. This is the first time this statistical difference is demonstrated in optical wavelengths. The polarization fraction distributions of both samples are well described by exponential distributions with averages of < p > =6.4 ^{+0.9}_{-0.8}× 10^{-2} for gamma-ray-loud blazars, and < p > =3.2 ^{+2.0}_{-1.1}× 10^{-2} for gamma-ray-quiet blazars. The most probable value for the difference of the means is 3.4^{+1.5}_{-2.0}× 10^{-2}. The distribution of polarization angles is statistically consistent with being uniform.
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| 2. |
- Pelgrims, Vincent, et al.
(författare)
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Starlight-polarization-based tomography of the magnetized ISM: PASIPHAE's line-of-sight inversion method
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 670
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Tidskriftsartikel (refereegranskat)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.
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