Reinforcement Learning based Multi-objective Optimization for Broadband Newtonian Noise Cancellation in GW Detectors

The sensitivity of terrestrial Gravitational-Wave detectors can be improved in the low-frequency region by sub-Tracting Newtonian Noise at the mirrors of the interferometer. An estimate of the Newtonian Noise is obtained by gathering information from an array of sensors monitoring the sources of noise. Efficient and maximal subtraction of Newtonian Noise is possible when the position of the sensors is optimized for a wide range of frequencies. This constitutes a multi-objective optimization problem which is solved by generating a Pareto optimal solution. Generally, multiple Pareto optimal solutions are generated, and further analysis is done to select the most suitable Pareto point for implementation. This paper proposes a method to obtain a smart Pareto optimal point by modifying the Normal Boundary Intersection method using reinforcement learning techniques. The proposed method will directly generate the smart Pareto point to be implemented in the Newtonian Noise cancellation system. The performance of our algorithm is compared with existing literature.