A spaceborne mounting method for fixing a 100 × 100 × 100 mm cubic Fabry–Pérot (FP) cavity is presented. The method constrains the FP cavity in eight directions with a titanium bracket, PEEK gaskets, and vacuum glue. Three criteria were proposed for judging whether the FP cavity is offset or not during aerospace vibration tests. Results indicate that the proposed method is a reliable, robust, and stable way to fix a cubic FP cavity in ultra-stable lasers (USLs). This approach paves the way for the use of USLs in space.

Acousto-optic modulators (AOMs) have been widely used in ultra-stable lasers (USLs) for optimizing its performances. A thermal theoretical model of the AOM, which is made by TeO₂, was established. Based on the model, the temperature coefficients of the diffraction angle and efficiency were calculated to be 4.051 μrad/°C and 0.018%/°C. The influences of thermal effects of the AOM on USLs’ cavity coupling and frequency stability were firstly studied. A 1 °C temperature change in the AOM results in a 0.31 Hz frequency fluctuation of the laser within the USL cavity. Simulation and experimental results indicate that, to achieve USLs’ optimal performance, thermal effects of AOMs within the system must be addressed and managed.

In this paper, the vibration sensitivity and zero-expansion temperature of a 100 mm cubic Fabry-Pérot (FP) cavity with reinforced-support are analyzed. Initially, the reinforced-support FP cavity was simulated and analyzed. Simulation results indicate that vibration sensitivity of the FP cavity is less than 1.5 × 10⁻¹⁰/g, and zero-expansion temperature of the FP cavity is influenced by the support structure by approximately 1 °C. Subsequently, experiments were carried out. The experimental results demonstrate that vibration sensitivities of the reinforced-support FP cavity in the Z, X, and Y directions are 1.73 × 10⁻¹⁰/g, 2.09 × 10⁻¹¹/g, and 2.13 × 10⁻¹¹/g, respectively. The zero-expansion temperature is about 29.5 °C. The vibration sensitivity of the reinforced-support FP cavity is comparable to that of the four-point support FP cavity, and zero-expansion temperature of the FP cavity is affected limitedly by the support structure. The same vibration sensitivity as the four-point support cavity and robust vibration resistance may expand the application of the transportable or spaceborne ultra-stable laser with cubic FP cavity.

The Fabry–Pérot (FP) cavity is the essential component of an ultra-stable laser (USL) for gravitational wave detection, which couples multiple physics fields (optical/thermal/mechanical) and requires ultra-high precision. Aiming at the deficiency of the current single physical field optimization, a multi-physics and multi-objective optimization method for fixing the cubic FP cavity based on data learning is proposed in this paper. A multi-physics coupling model for the cubic FP cavity is established and the performance is obtained via finite element analysis. The key performance indices (V, $w_F$, $w_F$) and key design variables (d, l, F) are determined considering the features of the FP cavity. Different data learning models (NN, RSF, KRG) are established and compared based on 49 sets of data acquired by orthogonal experiments, with the results showing that the neural network has the best performance. NSGA-II is adopted as the optimization algorithm, the Pareto optimal front is obtained, and the optimal combination of design variables is finally determined as $\{5,32,250\}$. The performance after optimization proves to be greatly improved, with the displacement under the fixing force and vibration test both decreased by more than 60%. The proposed optimization strategy can help in the design of the FP cavity, and could enlighten other optimization fields as well.

Fabry-Pérto (FP) cavity is the essential component of ultra-stable laser (USL) for gravitational wave detection, which couples multi-physic (Optical/Thermal/Mechanics) fields and requires ultra-high precision. To satisfy the requirements of precise and efficient design, a multi-physic coupling optimization method for fixing cubic FP cavity based on data learning is proposed. A multi-physic model for the cubic FP cavity is established and the performance is obtained by finite element analysis. The key performance indices (V, wF, wF) and key design variables (d, l, F) are determined considering the features of the FP cavity. The 49 sets of data by orthogonal experiment are acquired for the establishment and comparison of different data learning models (NN, RSF, KRG). The result turns out that the neural network has the best performance. Based on NSGA-II, the Pareto optimal front is obtained and the optimal combination of design variables is finally determined as {5,32,250}. The performance after optimization has proven to be a great improvement, of which the displacement under the fixing force and vibration test are decreased by more than 60%. The optimization strategy can not only help the design of the FP cavity but also enlighten other optimization fields.