Cross-entropy optimiser: a new tool to study precession in astrophysical jets

TL;DR

This paper introduces a novel application of the cross-entropy optimization method to analyze jet precession in astrophysical sources, effectively identifying precession parameters from observational data despite sampling and identification challenges.

Contribution

The study presents the first use of the cross-entropy method for modeling jet precession, demonstrating its effectiveness in accurately recovering parameters from synthetic data.

Findings

Successfully recovered precession parameters within 1% accuracy in tests

Identified non-precessing jets correctly as non-precessing

Proved robustness of the method against sampling and identification issues

Abstract

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 optimisation. 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. Aiming to recover these parameters, we applied the cross-entropy method to our precession model, varying exhaustively the quantities associated to 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 1%-level. Even for a non-precessing jet, our optimization method could point out successfully the lack of precession.