Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios
We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the computational cost of inference and preserving some or all of the reliability guarantees of parametric methods. The first approach is broadly applicable, while the second is tailored to a class of particle physics analyses that admit a semi-parametric NSBI formulation. With only a modest compromise in raw sensitivity, these methods represent an important step toward computationally efficient NSBI in offline analyses and also open the door to the exploration of trigger-level applications in the future. Based on our comparison studies, we recom...
| Authors | Sean Benevedes, Mani Dehghan, Aishik Ghosh et al. |
|---|---|
| Primary category | hep-ph |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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