A box-fitting algorithm in the search for periodic transits

TLDR

The study aims to characterize a box‑fitting algorithm for detecting periodic transits in stellar photometry and to predict detection significance across signal parameters. The algorithm identifies periodic transits by fitting a box shape that alternates between two levels, spending most of the time at the higher level. The effective signal‑to‑noise ratio is the key metric; values above 6 yield significant detections, and the box‑fitting algorithm outperforms existing methods, particularly at low SNR.

Abstract

We study the statistical characteristics of a box-fitting algorithm to analyze stellar photometric time series in the search for periodic transits by extrasolar planets. The algorithm searches for signals characterized by a periodic alternation between two discrete levels, with much less time spent at the lower level. We present numerical as well as analytical results to predict the possible detection significance at various signal parameters. It is shown that the crucial parameter is the effective signal-to-noise ratio – the expected depth of the transit divided by the standard deviation of the measured photometric average within the transit. When this parameter exceeds the value of 6 we can expect a significant detection of the transit. We show that the box-fitting algorithm performs better than other methods available in the astronomical literature, especially for low signal-to-noise ratios.