Variable classification in the LSST era: Exploring a model for quasi-periodic light curves

Understand

LSST is expected to yield ~10^7 light curves over the course of its mission, which will require a concerted effort in automated classification.

• Stochastic processes provide one means of quantitatively describing variability with the potential advantage over simple light curve statistics that the parameters may be physically meaningful.
• Here, we survey a large sample of periodic, quasi-periodic, and stochastic OGLE-III variables using the damped random walk (DRW, CARMA(1,0)) and quasi-periodic oscillation (QPO, CARMA(2,1)) stochastic process models.
• The QPO model is described by an amplitude, a period, and a coherence time-scale, while the DRW has only an amplitude and a time-scale.