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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.16343 |
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| _version_ | 1866912826115751936 |
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| author | Law, Kai Hong |
| author_facet | Law, Kai Hong |
| contents | A selection of new results from the CMS experiment is presented. These results focus on searches for dark-sector particles using Run 2 or Run 3 data. Dedicated data streams were utilised to explore the low-mass parameter space. Machine learning techniques were employed to discriminate between signal and background. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring new physics in the dark sector at CMS Law, Kai Hong High Energy Physics - Experiment A selection of new results from the CMS experiment is presented. These results focus on searches for dark-sector particles using Run 2 or Run 3 data. Dedicated data streams were utilised to explore the low-mass parameter space. Machine learning techniques were employed to discriminate between signal and background. |
| title | Exploring new physics in the dark sector at CMS |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2512.16343 |