カテゴリー: astro-informatics

Optical polarization variations in the blazar PKS 1749+096

Uemura, M., Itoh, R., Liodakis, I., Blinov, D., Nakayama, M., Xu, L., Sawada, N., Wu, H.-Y., and Fujishiro, I., “Optical polarization variations in the blazar PKS 1749+096”, PASJ, 69, 96, 2017

We report on the variation in the optical polarization of the blazar PKS 1749+096. The object favors a polarization angle (PA) of 40°-50° at the flare maxima, which is close to the position angle of the jet (20°-40°). Three clear polarization rotations were detected in the negative PA direction associated with flares. The light-curve maxima of the flares possibly tended to lag behind the PD maxima and color-index minima. We propose a scenario to explain these observational features, where transverse shocks propagate along curved trajectories. The favored PA at the flare maxima suggests that the observed variations were governed by the variations in the Doppler factor, δ. Based on this scenario, the minimum viewing angle of the source, θ _min = 4.8°-6.6°, and the location of the source, Δr ≳ 0.1 pc, from the central black hole were estimated. The combined effect of the variation in δ and acceleration/cooling of electrons is probably responsible for the observed diversity of the polarization variations in the flares.

TimeTubes: Visualization of Polarization Variations in Blazars

M. Uemura, R. Itoh, L. Xu, M. Nakayama, H.-Y. Wu, K. Watanabe, S. Takahashi, and I. Fujishiro, “TimeTubes: Visualization of Polarization Variations in Blazars”, Galaxies, 4, 23, 2016

Optical polarization provides important clues to the magnetic field in blazar jets. It is easy to find noteworthy patterns in the time-series data of the polarization degree (PD) and position angle (PA). On the other hand, we need to see the trajectory of the object in the Stokes QU plane when the object has multiple polarized components. In this case, ironically, the more data we have, the more difficult it is to gain any knowledge from it. Here, we introduce TimeTubes, a new visualization scheme to explore the time-series data of polarization observed in blazars. In TimeTubes, the data is represented by tubes in 3D (Q, U, and time) space. The measurement errors of Q and U, color, and total flux of objects are expressed as the size, color, and brightness of the tubes. As a result, TimeTubes allows us to see the behavior of six variables in one view. We used TimeTubes for our data taken by the Kanata telescope between 2008 and 2014. We found that this tool facilitates the recognition of the patterns in blazar variations; for example, favored PA of flares and PA rotations associated with a series of flares.

Data-driven approach to Type Ia supernovae

M. Uemura, K. S. Kawabata, S. Ikeda, K. Maeda, H. Y. Wu, K. Watanabe, S. Takahashi, and I. Fujishiro, “Data-driven approach to Type Ia supernovae: variable selection on the peak luminosity and clustering in visual analytics”, Journal of Physics: Conference Series, 699, id. 012009, 2016

 

Type Ia supernovae (SNIa) have an almost uniform peak luminosity, so that they are used as “standard candle” to estimate distances to galaxies in cosmology. In this article, we introduce our two recent works on SNIa based on data-driven approach. The diversity in the peak luminosity of SNIa can be reduced by corrections in several variables. The color and decay rate have been used as the explanatory variables of the peak luminosity in past studies. However, it is proposed that their spectral data could give a better model of the peak luminosity. We use cross-validation in order to control the generalization error and a LASSO-type estimator in order to choose the set of variables. Using 78 samples and 276 candidates of variables, we confirm that the peak luminosity depends on the color and decay rate. Our analysis does not support adding any other variables in order to have a better generalization error. On the other hand, this analysis is based on the assumption that SNIa originate in a single population, while it is not trivial. Indeed, several sub-types possibly having different nature have been proposed. We used a visual analytics tool for the asymmetric biclustering method to find both a good set of variables and samples at the same time. Using 14 variables and 132 samples, we found that SNIa can be divided into two categories by the expansion velocity of ejecta. Those examples demonstrate that the data-driven approach is useful for high-dimensional large-volume data which becomes common in modern astronomy.

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