Scaling Particle Collision Data Analysis
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910735649472512 |
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| author | Wu, Hengkui Chi, Panpan Zhu, Yongfeng Liu, Liujiang Hu, Shuyang Wang, Yuexin Zhou, Chen Wang, Qihao Xin, Yingsi Liu, Bruce Liang, Dahao Jia, Xinglong Ruan, Manqi |
| author_facet | Wu, Hengkui Chi, Panpan Zhu, Yongfeng Liu, Liujiang Hu, Shuyang Wang, Yuexin Zhou, Chen Wang, Qihao Xin, Yingsi Liu, Bruce Liang, Dahao Jia, Xinglong Ruan, Manqi |
| contents | For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimental high energy physics. This limitation is primarily due to BPE tokenization's inefficacy with numerical data. In this paper, we propose a task-agnostic architecture, BBT-Neutron, which employs a binary tokenization method to facilitate pretraining on a mixture of textual and large-scale numerical experimental data. We demonstrate the application of BBT-Neutron to Jet Origin Identification (JoI), a critical categorization challenge in high-energy physics that distinguishes jets originating from various quarks or gluons. Our results indicate that BBT-Neutron achieves comparable performance to state-of-the-art task-specific JoI models. Furthermore, we examine the scaling behavior of BBT-Neutron's performance with increasing data volume, suggesting the potential for BBT-Neutron to serve as a foundational model for particle physics data analysis, with possible extensions to a broad spectrum of scientific computing applications for Big Science experiments, industrial manufacturing and spacial computing. The project code is available at https://github.com/supersymmetry-technologies/bbt-neutron. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_00129 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scaling Particle Collision Data Analysis Wu, Hengkui Chi, Panpan Zhu, Yongfeng Liu, Liujiang Hu, Shuyang Wang, Yuexin Zhou, Chen Wang, Qihao Xin, Yingsi Liu, Bruce Liang, Dahao Jia, Xinglong Ruan, Manqi Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimental high energy physics. This limitation is primarily due to BPE tokenization's inefficacy with numerical data. In this paper, we propose a task-agnostic architecture, BBT-Neutron, which employs a binary tokenization method to facilitate pretraining on a mixture of textual and large-scale numerical experimental data. We demonstrate the application of BBT-Neutron to Jet Origin Identification (JoI), a critical categorization challenge in high-energy physics that distinguishes jets originating from various quarks or gluons. Our results indicate that BBT-Neutron achieves comparable performance to state-of-the-art task-specific JoI models. Furthermore, we examine the scaling behavior of BBT-Neutron's performance with increasing data volume, suggesting the potential for BBT-Neutron to serve as a foundational model for particle physics data analysis, with possible extensions to a broad spectrum of scientific computing applications for Big Science experiments, industrial manufacturing and spacial computing. The project code is available at https://github.com/supersymmetry-technologies/bbt-neutron. |
| title | Scaling Particle Collision Data Analysis |
| topic | Machine Learning High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2412.00129 |