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May 8 – 12, 2023
Norfolk Waterside Marriott
US/Eastern timezone

JetNet library for machine learning in high energy physics

May 11, 2023, 11:15 AM
Hampton Roads VII (Norfolk Waterside Marriott)

Hampton Roads VII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning


Pareja, Carlos


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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Presentation materials