Kavli Affiliate: Lisa Barsotti
| First 5 Authors: Chris Whittle, Ge Yang, Matthew Evans, Lisa Barsotti,
| Summary:
Machine learning has become an effective tool for processing the extensive
data sets produced by large physics experiments. Gravitational-wave detectors
are now listening to the universe with quantum-enhanced sensitivity,
accomplished with the injection of squeezed vacuum states. Squeezed state
preparation and injection is operationally complicated, as well as highly
sensitive to environmental fluctuations and variations in the interferometer
state. Achieving and maintaining optimal squeezing levels is a challenging
problem and will require development of new techniques to reach the lofty
targets set by design goals for future observing runs and next-generation
detectors. We use machine learning techniques to predict the squeezing level
during the third observing run of the Laser Interferometer Gravitational-Wave
Observatory (LIGO) based on auxiliary data streams, and offer interpretations
of our models to identify and quantify salient sources of squeezing
degradation. The development of these techniques lays the groundwork for future
efforts to optimize squeezed state injection in gravitational-wave detectors,
with the goal of enabling closed-loop control of the squeezer subsystem by an
agent based on machine learning.
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