Optical Cavity Stabilisation to Study Macroscopic Quantum Mechanics

Continual improvements in gravitational wave detector design have led to a gradual suppression of many, previously dominant, classical noise sources. Future developments in this field as well as construction of next generation detectors will lead to quantum noise mitigation becoming essential. The primary quantum noise sources-radiation pressure noise, dominant at low frequencies and shot noise, dominant at high frequencies-are already of interest in existing detectors, with aLIGO being limited by shot noise at frequencies above 200 Hz. The combined quantum noise spectrum can be shifted by tuning of the input laser power and the locus of points where the combined spectrum is minimised for any given input power is called the standard quantum limit (SQL). Being the point of maximum sensitivity, it is of particular interest in the development of gravitational wave detectors but this point has not been reached yet. This research aims to design a tabletop experiment for reaching the SQL with a long term goal of understanding physics at the SQL. This will be achieved by constructing a cryogenic optical cavity, suspended by multiple isolation stages, in order to suppress thermal noise and seismic noise respectively. Such a cavity needs to be stabilised both in terms of its alignment and temperature. As such, this research particularly focuses on achieving the desired noise suppression with a feasible and controllable design. Reaching the SQL will provide both a fundamental as well as practical benefit to the scientific community. Experiments at the SQL will allow for an enhanced understanding of fundamental physics, whilst the techniques developed in reaching it may be of interest to designers of improved gravitational wave detectors.

The high precision measurements needed for gravitational wave detection can only be achieved through very tight control over the exact position of each optical component in the detector. The components are locked in place using many feedback and feedforward loops, which can automatically adjust for deviations from the operating point. These loops, however, rely on the components being brought close enough to the operating point as the control loops do not have a high dynamic range. To achieve this initial, approximate alignment (and realignment whenever the components drift outside of the range of the control loops), the components are aligned by eye. This can be a time consuming process, which also requires manual input, and results in increased down-time for the detectors. The beam shape in the detector is imaged at several output ports, which can then be viewed by a human observer and corrected. This research seeks to automate this process via the use of machine learning algorithms. A detector like aLIGO has numerous suspended optics that lead to many degrees of freedom. The resulting beam shape is thus complex, which means that only advanced pattern recognition systems like the human mind have the ability to use these effectively. With the recent growth of machine learning as an image classification and pattern recognition tool, we believe that this complex problem can be solved. By automating this process, we hope to achieve faster realignment and thus a reduction in detector down-time. This has the immediate benefit of increasing value for money, as the operation of the site continues to incur significant expenses even when the detector itself is inoperative. As a further improvement, this could lead to increased coincident observing time for multiple detectors. This would allow for much better extraction of information from simultaneous gravitational wave detection