Autoregressive planet search: irregular time series

Caceres et al. (2019AJ....158...58C) show that the autoregressive planet search (ARPS) method - a combination of autoregressive integrated moving average (ARIMA) parametric modeling, a new transit comb filter (TCF) periodogram, and machine learning classification - is effective when applied to evenly spaced light curves from space-based missions.

We investigate here whether ARIMA and TCF will be effective for ground-based survey light curves that are often sparsely sampled with high noise levels from atmospheric and instrumental conditions. The ARPS procedure is applied to selected light curves with strong planetary signals from the Kepler mission that have been altered to simulate the conditions of ground-based exoplanet surveys. Typical irregular cadence patterns are used from the Hungarian-made Automated Telescope Network-South (HATSouth) survey.