Work

Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios

Year: 2026

Type: preprint

Abstract: 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 recommend the use of our first approach, Latent Categories, for robust and efficient inference.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.36044

Authors: Benevedes, Sean, Dehghan, Mani, Ghosh, Aishik, Park, Tae Hyoun

Institutions: Georgia Institute of Technology, Lawrence Berkeley National Laboratory, Max Planck Institute for Physics

Language: English

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, National Energy Research Scientific Computing Center

OpenAlex ID: W7214970373

Cites: 24

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Related to: 10