Identification of light sources using machine learning
Identification of light sources using machine learning
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DOI：
10.1063/1.5133846
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发表时间：
2019-09
影响因子：
15
通讯作者：
Chenglong You;M. A. Quiroz-Juárez;A. Lambert;N. Bhusal;Chao Dong;A. Pérez-Leija;A. Javaid;
中科院分区：
文献类型：
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作者：
Chenglong You;M. A. Quiroz-Juárez;A. Lambert;N. Bhusal;Chao Dong;A. Pérez-Leija;A. Javaid;
光源的识别对于多种光子技术的发展来说是一项至关重要的任务。在过去的几十年里，对太阳光、激光辐射和分子荧光等多种光源的识别依赖于光子统计数据的收集或量子态断层扫描的实施。一般来说，这项任务需要大量的测量来揭示光的特征统计波动和相关特性，特别是在低光子通量范围内。在本文中，我们利用人工神经网络和朴素贝叶斯分类器的自学习功能来显着减少在单光子水平上区分热光和相干光所需的测量数量。我们在平均光子数低于 1 的情况下通过数十次测量证明了强大的光识别能力。我们的工作表明，相对于传统的光源表征方案，测量数量提高了几个数量级。我们的工作对激光雷达和显微镜等多种光子技术具有重要意义。
The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation and molecule fluorescence has relied on the collection of photon statistics or the implementation of quantum state tomography. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this article, we exploit the self-learning features of artificial neural networks and naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. Our work demonstrates an improvement in terms of the number of measurements of several orders of magnitude with respect to conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as LIDAR and microscopy.