Periodic transit and variability search with simultaneous systematics filtering: Is it worth it?

Abstract

By using subsets of the HATNet and K2 (Kepler two-wheel) Campaign 1 databases, we examine the effectiveness of filtering out systematics from photometric time series while simultaneously searching for periodic signals. We carry out tests to recover simulated sinusoidal and transit signals added to time series with both real and artificial noise. We find that the simple (and more traditional) method that performs correction for systematics first and signal search thereafter, produces higher signal recovery rates on the average, while also being substantially faster than the simultaneous method. Independently of the method of search, once the signal is found, a far less time consuming full-fledged model, incorporating both the signal and systematics, must be employed to recover the correct signal shape. As a by-product of the tests on the K2 data, we find that for longer period sinusoidal signals the detection rate decreases (after an optimum value is reached) as the number of light curves used for systematics filtering increases. The decline of the detection rate is observable in both methods of filtering, albeit the simultaneous method performs better in the regime of relative high template number. We suspect that the observed phenomenon is linked to the increased role of low amplitude intrinsic stellar variability in the space-based data. This assumption is also supported by the substantially higher stability of the detection rates for transit signals against the increase of the template number.

Are all RR Lyrae stars modulated?

“…As discussed by Kovacs et al (2016), when these methods are used for period search for signals commensurable with the size of the systematics, due to the extra freedom introduced by the inclusion of the underlying (but unknown) signal, the resulting detection statistics become poorer than for the more standard methods, assuming no signal content (e.g., SysRem of Tamuz et al 2005).…”

Autoregressive Planet Search: Methodology

“…After stellar variability has been significantly reduced from the time series with autoregressive modeling, we are left with the main task of finding planetary transits in the model residuals ( §3). In accord with the analysis of Kovács et al (2016), we treat the search for periodic transits as a stage of analysis distinct from stellar variability reduction, as there are disadvantages of combining these stages. We adopt the basic approach of Box-fitting Least-Squares by applying a matched filter to a simplified box-shaped transit.…”

More planetary candidates from K2 Campaign 5 using TRAN_K2

“…tran_k2_v6 ponent, such the self-adjusted weights on outliers used in this paper. There have been attempts to avoid signal degradation that is due to the use of incomplete signal models (Foreman-Mackey et al 2015;Angus et al 2016;Taaki et al 2020), but a subsequent work by Kovacs et al (2016) showed that full modeling in a signal search is considerably less efficient than previously considered, and, obviously, more demanding in terms of CPU power.…”