Segmented-Polynomial-fitting Least Squares (SPLS): An Optimized Algorithm to Find Earth Twins

Abstract Detecting Earth twins remains challenging because their shallow, long-period transits are difficult to distinguish from background noise. Motivated by the challenge, we developed Segmented-Polynomial-fitting Least Squares (SPLS), a new algorithm that simultaneously fits planetary transits and background trends using a segmented double polynomial model. Prior to signal detection, the optimal polynomial order for the trend component is selected using Bayes factor-based model comparison. During the periodogram search, the Signal Detection Efficiency metric is used to assess signal significance. The algorithm is accelerated by a three-step approximation and nonlinear parameter sampling tailored to SPLS. We compare the performance of SPLS with traditional detrending-detection approaches across different orbital periods, signal-to-noise ratios (SNR), planet radii, and stellar noise levels on an injection–recovery test. When detecting signals with periods between 10 and 480 days and SNRs below 9, SPLS achieves at least a 22.6% higher true positive rate than other methods at the same 10% false positive rate. Using the threshold determined from the Receiver Operating Characteristic curve analysis, our method also recovers the most true signals while yielding the fewest false positives among all injected samples, and reaches a 97% recovery fraction in Kepler confirmed single-planet systems. The tests demonstrate that SPLS improves the detection of transiting planets, particularly for low-SNR, long-period signals. It offers the potential for finding Earth twins in future applications to data from Kepler, TESS, and upcoming PLATO and Earth 2.0 missions.