Bayesian Approach to Find a Long-Term Trend in Erratic Polarization Variations Observed in Blazars
Makoto Uemura, Koji S. Kawabata, Mahito Sasada, Yuki Ikejiri, Kiyoshi, Sakimoto, Ryosuke Itoh, Masayuki Yamanaka, Takashi Ohsugi, Shuji Sato, and, Masaru Kino

TL;DR
This paper introduces a Bayesian method to extract long-term polarization trends from erratic blazar data, revealing systematic behaviors in some objects and complex short-term variations in others.
Contribution
A novel Bayesian approach for separating long-term polarization trends from short-term variations in blazars, validated with artificial data and applied to multiple sources.
Findings
Successfully separated long-term polarization components in two blazars.
Detected no long-term trend in S5 0716+714, indicating complex short-term behavior.
Revealed that erratic polarization variations can be due to underlying long-term components.
Abstract
We developed a method to separate a long-term trend from observed temporal variations of polarization in blazars using a Bayesian approach. The temporal variation of the polarization vector is apparently erratic in most blazars, while several objects occasionally exhibited systematic variations, for example, an increase of the polarization degree associated with a flare of the total flux. We assume that the observed polarization vector is a superposition of distinct two components, a long-term trend and a short-term variation component responsible for short flares. Our Bayesian model estimates the long-term trend which satisfies the condition that the total flux correlates with the polarized flux of the short-term component. We demonstrate that assumed long-term polarization components are successfully separated by the Bayesian model for artificial data. We applied this method to…
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Taxonomy
TopicsAstrophysics and Cosmic Phenomena · Radio Astronomy Observations and Technology · Neutrino Physics Research
