Speaker
Description
Machine learning (ML) has become ubiquitous in high energy physics (HEP) for many tasks, including classification, regression, reconstruction, and simulations. To facilitate development in this area, and to make such research more accessible, and reproducible, we require standard, easy-to-access, datasets and metrics. To this end, we develop the open source Python JetNet library with easily accessible, standardised interfaces for particle cloud datasets, implementations for HEP evaluation and loss metrics, and more useful tools for ML in HEP. While still in the development stage, JetNet has already been widely used for several ML projects at the LHC, averaging 2,000 downloads per month, and being prominently featured at recent conferences such as ML4Jets, illustrating its significant contribution to making ML in HEP research more FAIR.
| Consider for long presentation | Yes |
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