Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties
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| Format: | Preprint |
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2025
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| author | He, Minxuan Krause, Claudius Wang, Daohan |
| author_facet | He, Minxuan Krause, Claudius Wang, Daohan |
| contents | We present a dedicated graph neural network (GNN)-based methodology for the extraction of the Higgs boson signal strength $μ$, incorporating systematic uncertainties. The architecture features two branches: a deterministic GNN that processes kinematic variables unaffected by nuisance parameters, and an uncertainty-aware GNN that handles inputs modulated by systematic effects through gated attention-based message passing. Their outputs are fused to produce classification scores for signal-background discrimination. During training we sample nuisance-parameter configurations and aggregate the loss across them, promoting stability of the classifier under systematic shifts and effectively decorrelating its outputs from nuisance variations. The resulting binned classifier outputs are used to construct a Poisson likelihood, which enables profile likelihood scans over signal strength, with nuisance parameters profiled out via numerical optimization. We validate this framework on the FAIR Universe Higgs Uncertainty Challenge dataset, yielding accurate estimation of signal strength $μ$ and its 68.27\% confidence interval, achieving competitive coverage and interval widths in large-scale pseudo-experiments. Our code "Systematics-Aware Graph Estimator" (SAGE) is publicly available. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_00672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties He, Minxuan Krause, Claudius Wang, Daohan High Energy Physics - Phenomenology High Energy Physics - Experiment We present a dedicated graph neural network (GNN)-based methodology for the extraction of the Higgs boson signal strength $μ$, incorporating systematic uncertainties. The architecture features two branches: a deterministic GNN that processes kinematic variables unaffected by nuisance parameters, and an uncertainty-aware GNN that handles inputs modulated by systematic effects through gated attention-based message passing. Their outputs are fused to produce classification scores for signal-background discrimination. During training we sample nuisance-parameter configurations and aggregate the loss across them, promoting stability of the classifier under systematic shifts and effectively decorrelating its outputs from nuisance variations. The resulting binned classifier outputs are used to construct a Poisson likelihood, which enables profile likelihood scans over signal strength, with nuisance parameters profiled out via numerical optimization. We validate this framework on the FAIR Universe Higgs Uncertainty Challenge dataset, yielding accurate estimation of signal strength $μ$ and its 68.27\% confidence interval, achieving competitive coverage and interval widths in large-scale pseudo-experiments. Our code "Systematics-Aware Graph Estimator" (SAGE) is publicly available. |
| title | Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.00672 |