Learning convergence prediction of astrobots in multi-object spectrographs

TL;DR

This paper introduces a machine learning approach using support vector machines to predict the convergence of astrobot swarms in multi-object spectrographs, improving verification efficiency and handling complex scenarios.

Contribution

It develops a predictive model for astrobot swarm convergence, outperforming existing methods and extending to generalized scenarios with diverse parities.

Findings

Support vector machine model achieves better prediction accuracy.

The model effectively handles complex, large-scale swarm scenarios.

Predictions are collision-free and computationally efficient.

Abstract

Astrobot swarms are used to capture astronomical signals to generate the map of the observable universe for the purpose of dark energy studies. The convergence of each swarm in the course of its coordination has to surpass a particular threshold to yield a satisfactory map. The current coordination methods do not always reach desired convergence rates. Moreover, these methods are so complicated that one cannot formally verify their results without resource-demanding simulations. Thus, we use support vector machines to train a model which can predict the convergence of a swarm based on the data of previous coordination of that swarm. Given a fixed parity, i.e., the rotation direction of the outer arm of an astrobot, corresponding to a swarm, our algorithm reaches a better predictive performance compared to the state of the art. Additionally, we revise our algorithm to solve a more generalized convergence prediction problem according to which the parities of astrobots may differ. We present the prediction results of a generalized scenario, associated with a 487-astrobot swarm, which are interestingly efficient and collision-free given the excessive complexity of this scenario compared to the constrained one.