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Light curves of astrophysical objects frequently contain strictly periodicsignals. In those cases we can use that property to aid the detrendingalgorithm to fully disentangle an unknown periodic signal and an unknownbaseline signal with no power at that period. The periodic signal is modeled asa discrete probability distribution function (pdf), while the baseline signalis modeled as a residual timeseries. Those two components are disentangled byminimizing the length of the residual timeseries w.r.t. the per-bin pdf fluxes.We demonstrate the use of the algorithm on a synthetic case, on the eclipsingbinary KIC 3953981 and on the eccentric ellipsoidal variable KIC 3547874. Wefurther discuss the parameters and the limitations of the algorithm andspeculate on the two most common use cases: detrending the periodic signal ofinterest and measuring the dependence of instrumental response on controlledinstrumental variables. A more sophisticated version of the algorithm isreleased as open source on github and available via pip.

Finding the Needle in a Haystack: Detrending Photometric Timeseries Data of Strictly Periodic Astrophysical Objects