Modeling the Variability of Active Galactic Nuclei by Infinite Mixture of Ornstein-Uhlenbeck(OU) Processes. (arXiv:1811.03837v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Takata_T/0/1/0/all/0/1">Tadafumi Takata</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mukuta_Y/0/1/0/all/0/1">Yusuke Mukuta</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mizumoto_Y/0/1/0/all/0/1">Yoshihiko Mizumoto</a>
We develop an infinite mixture model of Ornstein-Uhlenbeck(OU) processes for
describing the optical variability of QSOs based on treating the variability as
a stochastic process. This enables us to get the parameters of the power
spectral densities(PSDs) on their brightness variations by providing more
flexible description of PSDs than the models based on single OU process(damped
random walk). We apply this model to 67,507 variable objects extracted from
SDSS Stripe82 photometric data and succeed in showing very high precision in
identifying QSOs (~99% levels in completeness and purity) among variable
objects based only on their variability, by investigating on 9,855
spectroscopically confirmed objects(7,714 QSOs and 2,141 stars) in the data of
SDSS Data Release 12(DR12), with sufficient and accurate multiple measurements
of their brightness. By comparing our results with the values based on other
models that are used in previous research, it is revealed that our model can be
used as the most effective method for selecting QSOs from variable object
catalog, especially regarding completeness and purity. The main reason of
improved identification rates are the ability of our model to separate clearly
QSOs and stars, especially on the small fraction of QSOs with variabilities
which can be described better than simple damped random walk model.
We develop an infinite mixture model of Ornstein-Uhlenbeck(OU) processes for
describing the optical variability of QSOs based on treating the variability as
a stochastic process. This enables us to get the parameters of the power
spectral densities(PSDs) on their brightness variations by providing more
flexible description of PSDs than the models based on single OU process(damped
random walk). We apply this model to 67,507 variable objects extracted from
SDSS Stripe82 photometric data and succeed in showing very high precision in
identifying QSOs (~99% levels in completeness and purity) among variable
objects based only on their variability, by investigating on 9,855
spectroscopically confirmed objects(7,714 QSOs and 2,141 stars) in the data of
SDSS Data Release 12(DR12), with sufficient and accurate multiple measurements
of their brightness. By comparing our results with the values based on other
models that are used in previous research, it is revealed that our model can be
used as the most effective method for selecting QSOs from variable object
catalog, especially regarding completeness and purity. The main reason of
improved identification rates are the ability of our model to separate clearly
QSOs and stars, especially on the small fraction of QSOs with variabilities
which can be described better than simple damped random walk model.
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