Giulia DeSalvo

Precise Measurement of Laser Power using an Optomechanical System

This paper shows a novel method to precisely measure the laser power using an optomechanical system. By measuring a mirror displacement caused by the reflection of an amplitude modulated laser beam, the number of photons in the incident continuous-wave laser can be precisely measured. We have demonstrated this principle by means of a prototype experiment uses a suspended 25 mg mirror as an mechanical oscillator coupled with the radiation pressure and a Michelson interferometer as the displacement sensor. A measurement of the laser power with an uncertainty of less than one percent (1σ) is achievable.

Learning with Rejection

We introduce a novel framework for classification with a rejection option that consists of simultaneously learning two functions: a classifier along with a rejection function. We present a full theoretical analysis of this framework including new data-dependent learning bounds in terms of the Rademacher complexities of the classifier and rejection families as well as consistency and calibration results. These theoretical guarantees guide us in designing new algorithms that can exploit different kernel-based hypothesis sets for the classifier and rejection functions. We compare and contrast our general framework with the special case of confidence-based rejection for which we devise alternative loss functions and algorithms as well. We report the results of several experiments showing that our kernel-based algorithms can yield a notable improvement over the best existing confidence-based rejection algorithm.

Learning with Deep Cascades

We introduce a broad learning model formed by cascades of predictors, Deep Cascades, that is structured as general decision trees in which leaf predictors or node questions may be members of rich function families. We present new data-dependent theoretical guarantees for learning with Deep Cascades with complex leaf predictors and node questions in terms of the Rademacher complexities of the sub-families composing these sets of predictors and the fraction of sample points reaching each leaf that are correctly classified. These guarantees can guide the design of a variety of different algorithms for deep cascade models and we give a detailed description of two such algorithms. Our second algorithm uses as node and leaf classifiers SVM predictors and we report the results of experiments comparing its performance with that of SVM combined with polynomial kernels.

High accuracy measurement of the quantum efficiency using radiation pressure

Preliminary investigations of a novel method to measure the laser power accurately using the radiation pressure are reported here. We aim to measure the laser power within one percent error to then obtain an accurate quantum efficiency (QE) of a photodiode. Since the typical error of QE is still a few percent due to the uncertainty of measured laser power, an accurate measurement of the laser power contributes a precise estimation of the QE. Our experimental setup is a suspended Michelson interferometer, where one of the pendulums is small, consisting of a 20-mg mirror and 10-um fiber. The motion of this small mirror is very sensitive to changes in radiation pressure. Due to this, the number of photons in the incident (intensity modulated) laser beam can be counted accurately by measuring displacement of the mirror. We set up the apparatus, and have found a suitable frequency band for the accurate measurement. Displacement caused by the radiation pressure was observed using the feedback signal.