Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios
Authors: Sean Benevedes, Mani Dehghan, Aishik Ghosh, Tae Hyoun Park
Organizations: School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA · School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA · Max Planck Institute for Physics, Boltzmannstr. 8, 85748 Garching, Germany
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.
Figures & tables
Figure 1: Schematic diagram of the training and inference pipelines for the Latent Categories approach. Black indicates components of the architecture present at both training and inference time. Orange indicates training-specific elements, showing how the encoder is trained as a multiclassifier, and green shows the inference pipeline.
Figure 2: Schematic diagram of the training and inference pipelines for the Mixture of Summary Statistics approach. As with Fig. 1 , orange shows how the model is trained and green shows how it is used for inference.
Figure 3: Sensitivity of LC, NSBI, and histogram analyses on the Gaussian example dataset. Narrower curves indicate better sensitivity. The true value of the parameter is shown with a vertical dotted line, and the 1σ and 2σ thresholds are shown with the horizontal dashed and dotted lines.
Figure 4: Sensitivity of LC, MSS, NSBI, and histogram analyses on the off-shell Higgs dataset. Narrower curves indicate better sensitivity. The true value of the parameter is shown with a vertical dotted line, and the 1σ and 2σ thresholds are shown with the horizontal dashed and dotted lines.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Expected occupancy of each Latent Categories bin for the nominal parameter value θ=0 and the boundary values θ=±1 in the Gaussian case study. The categories, which are intrinsically unordered, are sorted here in order of descending occupancy in the θ=0 case.
Figure 6: Expected occupancy of each Latent Categories bin for the nominal parameter value μ=1 and the boundary values μ=0,4 in the physics case study. The categories, which are intrinsically unordered, are sorted here in order of descending occupancy in the μ=1 case.
Figure 7: A plot of observed versus expected coverage for the Gaussian case study at θ=0 . The dotted black line shows nominal coverage, the dashed gray line shows the coverage attained using the exact likelihood, and each of the solid lines corresponds to coverage of one of the procedures considered in the main text. The shading corresponds to the 1σ uncertainty in the observed coverage corresponding to the finite number of pseudo-experiments.
Figure 8: A plot of observed versus expected coverage for the physics case study at μ=10 . The dotted black line shows nominal coverage, the dashed gray line shows the coverage attained using the exact likelihood, and each of the solid lines corresponds to coverage of one of the procedures considered in the main text (except for the “MSS 3 bins” procedure, which is only considered in this appendix). The shading corresponds to the 1σ uncertainty in the observed coverage corresponding to the finite number of pseudo-experiments.
ELLIS Institute Tübingen, Max Planck Institute for Intelligent Systems, Tübingen AI Center, Tübingen, Germany · CP3, Université catholique de Louvain, Louvain-la-Neuve, Belgium · Montefiore Institute, University of Liège, Belgium