Simultaneous adjustment of accelerator alignment reference networks based on the classical stochastic model

Abstract

Simultaneous adjustment is a critical technique for constructing high-precision accelerator alignment reference networks, which integrates measurements from multiple laser trackers. To address the specialized stochastic model used by the Unified Spatial Metrology Network (USMN) in SpatialAnalyzer® (SA), this study proposes a simultaneous adjustment method based on the classical stochastic model. Different from USMN, which converts distance residuals into angular values to unify the objective function, the proposed method directly optimizes observations using the nominal instrument accuracy, eliminating unit conversion and ensuring a consistent objective function through the coupling of residuals and the stochastic model. Validation using real measurement data from the HIAF-BRing alignment network shows that the proposed method achieves accuracy comparable to USMN, with a more concentrated angular residual distribution and fewer outliers. This method provides a theoretically rigorous and easy-to-implement alternative for data processing in accelerator alignment.