Discriminative versus generative approaches to simulation-based inference
Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimen...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adf68b |
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| author | Benjamin Sluijter Sascha Diefenbacher Wahid Bhimji Benjamin Nachman |
| author_facet | Benjamin Sluijter Sascha Diefenbacher Wahid Bhimji Benjamin Nachman |
| author_sort | Benjamin Sluijter |
| collection | DOAJ |
| description | Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice. |
| format | Article |
| id | doaj-art-2403e5b1dae1463993932faa75b6ae71 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-2403e5b1dae1463993932faa75b6ae712025-08-20T03:44:13ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303050310.1088/2632-2153/adf68bDiscriminative versus generative approaches to simulation-based inferenceBenjamin Sluijter0https://orcid.org/0009-0009-6830-9016Sascha Diefenbacher1https://orcid.org/0000-0003-4308-6804Wahid Bhimji2https://orcid.org/0000-0002-6213-8617Benjamin Nachman3https://orcid.org/0000-0003-1024-0932Leiden Institute of Physics, Universiteit Leiden , Leiden, RA 2300, The Netherlands; Physics Division, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, United States of AmericaPhysics Division, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, United States of AmericaNational Energy Research Scientific Computing Center, Berkeley Lab , Berkeley, CA 94720, United States of AmericaPhysics Division, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, United States of America; Fundamental Physics Directorate, SLAC National Accelerator Laboratory , Menlo Park, CA 94025, United States of America; Department of Particle Physics and Astrophysics, Stanford University , Stanford, CA 94305, United States of AmericaMost of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice.https://doi.org/10.1088/2632-2153/adf68bmachine learninguncertainty quantificationlikelihood estimationparticle physicshigh energy physics |
| spellingShingle | Benjamin Sluijter Sascha Diefenbacher Wahid Bhimji Benjamin Nachman Discriminative versus generative approaches to simulation-based inference Machine Learning: Science and Technology machine learning uncertainty quantification likelihood estimation particle physics high energy physics |
| title | Discriminative versus generative approaches to simulation-based inference |
| title_full | Discriminative versus generative approaches to simulation-based inference |
| title_fullStr | Discriminative versus generative approaches to simulation-based inference |
| title_full_unstemmed | Discriminative versus generative approaches to simulation-based inference |
| title_short | Discriminative versus generative approaches to simulation-based inference |
| title_sort | discriminative versus generative approaches to simulation based inference |
| topic | machine learning uncertainty quantification likelihood estimation particle physics high energy physics |
| url | https://doi.org/10.1088/2632-2153/adf68b |
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