标题： 利用异常检测在突破聆听观测中搜索技术特征 显示英文标题

标题： Using anomaly detection to search for technosignatures in Breakthrough Listen observations

摘要： 我们实现了一种机器学习算法，用于搜索通过突破监听合作组织使用帕克斯和格林班克望远镜对几百颗附近恒星进行的无线电观测中可能存在的地外技术特征信号。 检测技术的进步导致了数据量的指数级增长，这需要创新且高效的分析方法。 这个问题因地外信号可能采取的各种不同形式以及必须搜索的多维参数空间的大小而变得更加严重。 更糟糕的是，我们对这种信号性质的最佳猜测是它可能类似于人类技术和通信发出的信号，这是无线电观测中的主要（但多样化的）污染源。 我们通过结合模拟和异常检测的机器学习方法来应对这一挑战。 我们通过测量同一颗恒星连续光谱图之间的相似性，根据频率上的不寻常程度和时间上的持续性来对候选信号进行排名。 我们验证了与随机选择相比，我们的过滤器显著提高了被选中供人工审查的候选信号的质量。 在我们分析的大约10^11个光谱图中，我们目视检查了数千个最有希望的光谱图，并且为了验证目的还检查了数千个更多的光谱图，总计约20,000个，报告称没有一个候选信号经受住了基本的审查。 显示英文摘要

摘要： We implement a machine learning algorithm to search for extra-terrestrial technosignatures in radio observations of several hundred nearby stars, obtained with the Parkes and Green Bank Telescopes by the Breakthrough Listen collaboration. Advances in detection technology have led to an exponential growth in data, necessitating innovative and efficient analysis methods. This problem is exacerbated by the large variety of possible forms an extraterrestrial signal might take, and the size of the multidimensional parameter space that must be searched. It is then made markedly worse by the fact that our best guess at the properties of such a signal is that it might resemble the signals emitted by human technology and communications, the main (yet diverse) contaminant in radio observations. We address this challenge by using a combination of simulations and machine learning methods for anomaly detection. We rank candidates by how unusual they are in frequency, and how persistent they are in time, by measuring the similarity between consecutive spectrograms of the same star. We validate that our filters significantly improve the quality of the candidates that are selected for human vetting when compared to a random selection. Of the ~ 10^11 spectrograms that we analyzed, we visually inspected thousands of the most promising spectrograms, and thousands more for validation, about 20,000 in total, and report that no candidate survived basic scrutiny.