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Evidence of jet precession in many galactic and extragalactic sources has been reported in the literature. Much of this evidence is based on studies of the kinematics of the jet knots, which depends on the correct identification of the components to determine their respective proper motions and position angles on the plane of the sky. Identification problems related to fitting procedures, as well as observations poorly sampled in time, may influence the follow‐up of the components in time, which consequently might contribute to a misinterpretation of the data. In order to deal with these limitations, we introduce a very powerful statistical tool to analyse jet precession: the cross‐entropy method for continuous multi‐extremal optimization. Only based on the raw data of the jet components (right ascension and declination offsets from the core), the cross‐entropy method searches for the precession model parameters that better represent the data. In this work we present a large number of tests to validate this technique, using synthetic precessing jets built from a given set of precession parameters. With the aim of recovering these parameters, we applied the cross‐entropy method to our precession model, varying exhaustively the quantities associated with the method. Our results have shown that even in the most challenging tests, the cross‐entropy method was able to find the correct parameters within a 1 per cent level. Even for a non‐precessing jet, our optimization method could point out successfully the lack of precession.

Cross‐entropy optimizer: a new tool to study precession in astrophysical jets