We present a machine learning (ML) based method for automated detection of Gamma-Ray Burst (GRB) candidate events in the range 60–250 keV from the AstroSat Cadmium Zinc Telluride Imager data. We use density-based spatial clustering to detect excess power and carry out an unsupervised hierarchical clustering across all such events to identify the different light curves present in the data. This representation helps us to understand the instrument’s sensitivity to the various GRB populations and identify the major non-astrophysical noise artefacts present in the data. We use Dynamic Time Warping (DTW) to carry out template matching, which ensures the morphological similarity of the detected events with known typical GRB light curves. DTW alleviates the need for a dense template repository often required in matched filtering like searches. The use of a similarity metric facilitates outlier detection suitable for capturing previously unmodelled events. We briefly discuss the characteristics of 35 long GRB candidates detected using the pipeline and show that with minor modifications such as adaptive binning, the method is also sensitive to short GRB events. Augmenting the existing data analysis pipeline with such ML capabilities alleviates the need for extensive manual inspection, enabling quicker response to alerts received from other observatories such as the gravitational-wave detectors.

中文翻译：

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AstroSat CZTI 数据中 GRB 检测的机器学习方法

我们提出了一种基于机器学习 (ML) 的方法，用于从 AstroSat 镉锌碲化物成像仪数据中自动检测 60–250 keV 范围内的伽马射线暴 (GRB) 候选事件。我们使用基于密度的空间聚类来检测多余的功率，并对所有此类事件进行无监督的层次聚类，以识别数据中存在的不同光变曲线。这种表示有助于我们了解仪器对各种 GRB 种群的敏感性，并识别数据中存在的主要非天体物理噪声伪影。我们使用动态时间扭曲（DTW）进行模板匹配，确保检测到的事件与已知的典型 GRB 光曲线的形态相似性。DTW 减轻了对匹配过滤（如搜索）中经常需要的密集模板存储库的需求。相似性度量的使用有助于异常值检测，适用于捕获以前未建模的事件。我们简要讨论了使用管道检测到的 35 个长 GRB 候选者的特征，并表明通过自适应分箱等微小修改，该方法对短 GRB 事件也很敏感。使用此类 ML 功能增强现有数据分析管道可减轻对大量人工检查的需求，从而能够更快地响应从其他天文台（例如引力波探测器）收到的警报。该方法对短 GRB 事件也很敏感。使用此类 ML 功能增强现有数据分析管道可减轻对大量人工检查的需求，从而能够更快地响应从其他天文台（例如引力波探测器）收到的警报。该方法对短 GRB 事件也很敏感。使用此类 ML 功能增强现有数据分析管道可减轻对大量人工检查的需求，从而能够更快地响应从其他天文台（例如引力波探测器）收到的警报。