arXiv:2606.14373v1 Announce Type: cross Abstract: The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks. We show that casting event reconstruction as a machine learning problem naturally produces such a shared representation. We repurpose a machine learning model trained for particle-flow reconstruction (MLPF) to perform three distinct analysis tasks: jet flavor identification, jet energy regression, and m
The paper demonstrates a novel application of machine learning to particle physics, capitalizing on advancements in AI models that can unify previously disparate data analysis tasks.
This development indicates a significant AI-driven improvement in how complex scientific data, particularly in high-energy physics, will be processed and understood, leading to faster scientific discovery.
Traditional, modular physics reconstruction steps are being replaced by a unified, machine-learned approach, offering a shared representation across analysis tasks.
- · High-energy physics researchers
- · AI/ML model developers
- · Scientific computing infrastructure providers
- · Traditional algorithmic reconstruction specialists
Physicists can perform more efficient and comprehensive analyses of collider events, accelerating discovery.
The methodology could be extended to other scientific domains requiring complex data reconstruction from raw sensor input.
This could lead to a shift in scientific education, with an increased focus on machine learning literacy for experimental scientists.
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Read at arXiv cs.LG