Speaker
Description
Anomaly detection in multivariate time series is crucial to ensure the quality of data coming from a physics experiment. Accurately identifying the moments when unexpected errors or defects occur is essential but challenging, as the types of anomalies are unknown beforehand and reliably labeled data is scarce. Additionally, the multi-dimensional nature of time series data adds to the problem’s complexity, as the correlations between different dimensions must be considered.
To address the lack and unreliability of labeled data, we produce synthetic data with the Lorenzetti Simulator, a general-purpose framework simulating a high energy experiment, where we introduce artificial anomalies in the calorimeter. By introducing artificial anomalies in the calorimeter, we can systematically evaluate the effectiveness and sensitivity of anomaly detection methods, including transformer-based and other deep learning models. The approach employed here is generic and can be adapted to various detector architectures and their potential defects.
| AI keywords | anomaly detection, transformers, unsupervised learning, dataset creation |
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