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Register a new userFeature Selection for Classification of Blazars Based on Optical Photometric and Polarimetric Time-Series Data
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Blazars can be divided into two subtypes, flat spectrum radio quasars (FSRQs) and BL Lac objects, which have been distinguished phenomenologically by the strength of their optical emission lines, while their physical nature and relationship are still not fully understood. In this paper, we focus on the differences in their variability. We characterize the blazar variability using the Ornstein-Uhlenbeck (OU) process, and investigate the features that are discriminative for the two subtypes. We used optical photometric and polarimetric data obtained with the 1.5-m Kanata telescope for 2008-2014. We found that four features, namely the variation amplitude, characteristic timescale, and non-stationarity of the variability obtained from the light curves and the median of the degree of polarization (PD), are essential for distinguishing between FSRQs and BL Lac objects. FSRQs are characterized by rare and large flares, while the variability of BL Lac objects can be reproduced with a stationary OU process with relatively small amplitudes. The characteristics of the variability are governed not by the differences in the jet structure between the subtypes, but by the peak frequency of the synchrotron emission. This implies that the nature of the variation in the jets is common in FSRQs and BL Lac objects. We found that BL Lac objects tend to have high PD medians, which suggests that they have a stable polarization component. FSRQs have no such component, possibly because of a strong Compton cooling effect in sub-pc scale jets.
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A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.
We describe the simulated data sample for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC), a publicly available challenge to classify transient and variable events that will be observed by the Large Synoptic Survey Telescope (LSST), a new facility expected to start in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to 2018 December 17, and included 1,094 teams competing for prizes. Here we provide details of the 18 transient and variable source models, which were not revealed until after the challenge, and release the model libraries at https://doi.org/10.5281/zenodo.2612896. We describe the LSST Operations Simulator used to predict realistic observing conditions, and we describe the publicly available SNANA simulation code used to transform the models into observed fluxes and uncertainties in the LSST passbands (ugrizy). Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of type Ia supernova used to measure properties of dark energy. Our simulation framework will continue serving as a platform to improve the PLAsTiCC models, and to develop new models.
After three years of polarimetric monitoring of blazars, the RoboPol project has uncovered several key characteristics of polarimetric rotations in the optical for these most variable sources. The most important of these is that polarization properties of the synchrotron emission in the optical appear to be directly linked with gamma-ray activity. In this paper, we discuss the evidence for this connection, as well as the broader features of polarimetric behavior in blazars that are key in making progress with theoretical modeling of blazar emission.
The Fermi-LAT has detected more than 3000 sources in the GeV $gamma$-ray regime. The majority are extra-galactic and these sources are dominated by blazars. However, $sim28$ per cent of the sources in Fermi 3LAC are listed as blazar candidates of uncertain type (BCU). Increasing the number of classified Fermi-LAT sources is important for improving our understanding of extra-galactic $gamma$-ray sources and can be used to search for new very high energy sources. We report on the optical spectroscopy of seven selected unclassified BCU sources during 2014 and 2015 undertaken using the SAAO 1.9-m and Southern African Large Telescope (SALT). Based on the identified spectral lines we have classified three of the sources as FSRQs and the remaining four as BL Lac objects, determining the redshift for four sources.
It is of great significance to identify the characteristics of time series to qualify their similarity. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from logistic map, chaotic logistic map, chaotic Henon map, chaotic Ikeda map, hyperchaotic generalized Henon map and hyperchaotic folded-tower map. Based on the similarity of motif profiles, we further propose to estimate the similarity coefficients between different time series and classify these time series with high accuracy. We further apply the motif analysis method to the UCR Time Series Classification Archive and provide evidence of good classification ability for some data sets. Our analysis shows that the proposed triadic time series motif analysis performs better than the classic dynamic time wrapping method in classifying time series for certain data sets investigated in this work.
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