High fidelity probe and mitigation of mirror thermal fluctuations

Thermal noise arising from mechanical loss in high reflective dielectric coatings is a significant source of noise in precision optical measurements. In particular, Advanced LIGO, a large scale interferometer aiming to observed gravitational wave, is expected to be limited by coating thermal noise in the most sensitive region around 30–300 Hz. Various theoretical calculations for predicting coating Brownian noise have been proposed. However, due to the relatively limited knowledge of the coating material properties, an accurate approximation of the noise cannot be achieved. A testbed that can directly observed coating thermal noise close to Advanced LIGO band will serve as an indispensable tool to verify the calculations, study material properties of the coating, and estimate the detector’s performance.

This dissertation reports a setup that has sensitivity to observe wide band (10Hz to 1kHz) thermal noise from fused silica/tantala coating at room temperature from fixed-spacer Fabry–Perot cavities. Important fundamental noises and technical noises associated with the setup are discussed. The coating loss obtained from the measurement agrees with results reported in the literature. The setup serves as a testbed to study thermal noise in high reflective mirrors from different materials. One example is a heterostructure of Al_xGa_1−xAs (AlGaAs). An optimized design to minimize thermo–optic noise in the coating is proposed and discussed in this work.

Essays in optimal resource allocation under uncertainty with capacity constraints

This thesis brings together four papers on optimal resource allocation under uncertainty with capacity constraints. The first is an extension of the Arrow-Debreu contingent claim model to a good subject to supply uncertainty for which delivery capacity has to be chosen before the uncertainty is resolved. The second compares an ex-ante contingent claims market to a dynamic market in which capacity is chosen ex-ante and output and consumption decisions are made ex-post. The third extends the analysis to a storable good subject to random supply. Finally, the fourth examines optimal allocation of water under an appropriative rights system.

Speculation-Aware Resource Allocation for Cluster Schedulers

Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation.

In this thesis, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. The algorithm is model predictive in nature, i.e., at every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptimality of this model predictive algorithm vanishes as time horizon expands in the average case analysis. Further, we prove strong concentration results on the distribution of the load variance obtained by model predictive deferrable load control. These concentration results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance. Finally, we evaluate the algorithm via trace-based simulations.