Engineering topics

Stoughton, Chris

Publications and source records attributed to Stoughton, Chris.

3 records

DOE OSTI2024

GQuEST Control System Resolution

GQuEST (Gravity from Quantum Entanglement of Space-Time), is a collaboration verifying a quantum gravity model by measuring holographic effects on a microscopic scale. Its interferometer apparatus filters excessive optical noise and requires a control system that can adjust the filtration to accommodate for frequency changes in the output light. We stress-test this control system by simulating filter behaviors with parameters in their outermost regimes, and we have determined a threshold that maintains precision while minimizing resource usage. These results are important for the successful operation of GQuEST's interferometer, enabling more accurate tests of quantum gravity models and thereby advancing our understanding of gravity at the quantum mechanical level.

Nguyen, David↗

DOE OSTI2024

GQuEST Control System Resolution

This is presentation display of the software theory research I have done this summer in the pursuit of constructing the most optimal FPGA for the GQuEST apparatus.

Nguyễn, David↗

DOE OSTI2024

Controlling optical-cavity locking using reinforcement learning

Abstract This study applies an effective methodology based on Reinforcement Learning to a control system. Using the Pound–Drever–Hall locking scheme, we match the wavelength of a controlled laser to the length of a Fabry-Pérot cavity such that the cavity length is an exact integer multiple of the laser wavelength. Typically, long-term drift of the cavity length and laser wavelength exceeds the dynamic range of this control if only the laser’s piezoelectric transducer is actuated, so the same error signal also controls the temperature of the laser crystal. In this work, we instead implement this feedback control grounded on Q-Learning. Our system learns in real-time, eschewing reliance on historical data, and exhibits adaptability to system variations post-training. This adaptive quality ensures continuous updates to the learning agent. This innovative approach maintains lock for eight days on average.

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