Fair Universe Higgs Uncertainty Challenge
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915833707495424 |
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| author | Chakkappai, Ragansu Bhimji, Wahid Calafiura, Paolo Chang, Po-Wen Chou, Yuan-Tang Diefenbacher, Sascha Dudley, Jordan Farrell, Steven Ghosh, Aishik Guyon, Isabelle Harris, Chris Hsu, Shih-Chieh Khoda, Elham E. Nachman, Benjamin Nugent, Peter Rousseau, David Thorne, Benjamin Ullah, Ihsan Zhang, Yulei |
| author_facet | Chakkappai, Ragansu Bhimji, Wahid Calafiura, Paolo Chang, Po-Wen Chou, Yuan-Tang Diefenbacher, Sascha Dudley, Jordan Farrell, Steven Ghosh, Aishik Guyon, Isabelle Harris, Chris Hsu, Shih-Chieh Khoda, Elham E. Nachman, Benjamin Nugent, Peter Rousseau, David Thorne, Benjamin Ullah, Ihsan Zhang, Yulei |
| contents | This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in $(H \rightarrow τ^+ τ^-)$ cross-section measurement. Participants were tasked with developing advanced analysis techniques capable of dealing with uncertainties in the input training data and providing credible confidence intervals. The accuracy of these intervals was evaluated using pseudo-experiments to assess correct coverage. The dataset is now published in Zenodo, and the winning submissions are fully documented. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22247 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fair Universe Higgs Uncertainty Challenge Chakkappai, Ragansu Bhimji, Wahid Calafiura, Paolo Chang, Po-Wen Chou, Yuan-Tang Diefenbacher, Sascha Dudley, Jordan Farrell, Steven Ghosh, Aishik Guyon, Isabelle Harris, Chris Hsu, Shih-Chieh Khoda, Elham E. Nachman, Benjamin Nugent, Peter Rousseau, David Thorne, Benjamin Ullah, Ihsan Zhang, Yulei High Energy Physics - Phenomenology High Energy Physics - Experiment This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in $(H \rightarrow τ^+ τ^-)$ cross-section measurement. Participants were tasked with developing advanced analysis techniques capable of dealing with uncertainties in the input training data and providing credible confidence intervals. The accuracy of these intervals was evaluated using pseudo-experiments to assess correct coverage. The dataset is now published in Zenodo, and the winning submissions are fully documented. |
| title | Fair Universe Higgs Uncertainty Challenge |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.22247 |