HIGHLIGHTS
- who: Barry M. Dillon and colleagues from the Institut, Heidelberg, Germany University of have published the paper: Self-supervised anomaly detection for new physics, in the Journal: (JOURNAL)
- what: The authors investigate a method of model-agnostic anomaly detection through studying jets collimated sprays of pArticles produced in high-energy collisions. In Sec III, the authors implement the contrastive learning method using a transformer neural_network to map the dijet events from the event space into a latent space and evaluate the efficiency of the encoding. A. Data selection and preparation For this paper, the authors . . .
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