arXiv:2512.24116v3 Announce Type: replace-cross Abstract: Parton Distribution Functions (PDFs) play a central role in describing experimental data at colliders and provide insight into the structure of nucleons. As the LHC enters an era of high-precision measurements, a robust PDF determination with a reliable uncertainty quantification has become mandatory in order to match the experimental precision. The NNPDF collaboration has pioneered the use of Machine Learning (ML) techniques for PDF determinations, using Neural Networks (NNs) to parametrise the unknown PDFs in a flexible and unbiased w
The LHC is entering an era of high-precision measurements, necessitating more robust and reliable methods for data interpretation, which ML techniques can provide.
Precise understanding of fundamental particle interactions through enhanced PDF determinations impacts high-energy physics, potentially leading to new discoveries and refining our understanding of matter.
The application of advanced ML/NN techniques for Parton Distribution Function (PDF) determinations will improve accuracy and uncertainty quantification in experimental data analysis at colliders.
- · High-Energy Physics researchers
- · Particle accelerator facilities like LHC
- · AI/ML research in scientific applications
- · Particle physics experimental collaborations
- · Traditional PDF determination methods
Improved precision in the interpretation of experimental data from particle colliders.
Potential for new discoveries in fundamental physics due to more accurate characterization of nucleon structure and particle interactions.
Broader adoption of AI/ML methods across other complex scientific data analysis challenges beyond particle physics.
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