Abstract
We present a novel solution to automated beam alignment optimization. This device is based on a Raspberry Pi computer, stepper motors, commercial optomechanics and electronic devices, and the open-source machine learning algorithm M-LOOP. We provide schematic drawings for the custom hardware necessary to operate the device and discuss diagnostic techniques to determine the performance. The beam auto-aligning device has been used to improve the alignment of a laser beam into a single-mode optical fiber from manually optimized fiber alignment, with an iteration time of typically 20minutes. We present example data of one such measurement to illustrate device performance.
Highlights
Machine learning (ML) methods can discover patterns in data without requiring any assumptions about the data’s structure.1 Performing research with ML began in earnest in the 1980s,2 and by 1992, ML methods were used to, for example, create non-intuitive laser pulse-sequences for exciting rotational quantum states.3 it is only in the last decade or so that ML methods have begun to be used more widely in the atomic, molecular, and optical (AMO) physics community
In the remainder of the paper, we illustrate the use of the beam auto-aligner
For ease of direct comparison between initial starting conditions, the parameter values are normalized as shown in Fig. 4, with each sub-figure having its own normalization, so that the parameters only ever takes a value between −1 and 1
Summary
Machine learning (ML) methods can discover patterns in data without requiring any assumptions about the data’s structure. Performing research with ML began in earnest in the 1980s,2 and by 1992, ML methods were used to, for example, create non-intuitive laser pulse-sequences for exciting rotational quantum states. it is only in the last decade or so that ML methods have begun to be used more widely in the atomic, molecular, and optical (AMO) physics community. Performing research with ML began in earnest in the 1980s,2 and by 1992, ML methods were used to, for example, create non-intuitive laser pulse-sequences for exciting rotational quantum states.. Performing research with ML began in earnest in the 1980s,2 and by 1992, ML methods were used to, for example, create non-intuitive laser pulse-sequences for exciting rotational quantum states.3 It is only in the last decade or so that ML methods have begun to be used more widely in the atomic, molecular, and optical (AMO) physics community. ML has recently even been used to create new quantum experiments: the system both learned to create a variety of entangled states and improved the efficiency of their realization.9 Despite these advances, no work using ML for beam alignment has been found ML techniques have been used to create self-tuning, mode-locked lasers; for automating the production of Bose–Einstein condensation; and maintaining doughnut-shaped beams in scattering media. ML has recently even been used to create new quantum experiments: the system both learned to create a variety of entangled states and improved the efficiency of their realization. Despite these advances, no work using ML for beam alignment has been found
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