Full event particle-level unfolding with variable-length latent variational diffusion
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown pro...
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| Format: | Article |
| Language: | English |
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SciPost
2025-04-01
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| Series: | SciPost Physics |
| Online Access: | https://scipost.org/SciPostPhys.18.4.117 |
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| author | Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson |
| author_facet | Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson |
| author_sort | Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson |
| collection | DOAJ |
| description | The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic $t\bar{t}$ production at the Large Hadron Collider. |
| format | Article |
| id | doaj-art-3a7f55c9959d47ba81f62ca80a691445 |
| institution | DOAJ |
| issn | 2542-4653 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SciPost |
| record_format | Article |
| series | SciPost Physics |
| spelling | doaj-art-3a7f55c9959d47ba81f62ca80a6914452025-08-20T03:06:52ZengSciPostSciPost Physics2542-46532025-04-0118411710.21468/SciPostPhys.18.4.117Full event particle-level unfolding with variable-length latent variational diffusionAlexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel WhitesonThe measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic $t\bar{t}$ production at the Large Hadron Collider.https://scipost.org/SciPostPhys.18.4.117 |
| spellingShingle | Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson Full event particle-level unfolding with variable-length latent variational diffusion SciPost Physics |
| title | Full event particle-level unfolding with variable-length latent variational diffusion |
| title_full | Full event particle-level unfolding with variable-length latent variational diffusion |
| title_fullStr | Full event particle-level unfolding with variable-length latent variational diffusion |
| title_full_unstemmed | Full event particle-level unfolding with variable-length latent variational diffusion |
| title_short | Full event particle-level unfolding with variable-length latent variational diffusion |
| title_sort | full event particle level unfolding with variable length latent variational diffusion |
| url | https://scipost.org/SciPostPhys.18.4.117 |
| work_keys_str_mv | AT alexandershmakovkevingreifmichaeljamesfentonaishikghoshpierrebaldidanielwhiteson fulleventparticlelevelunfoldingwithvariablelengthlatentvariationaldiffusion |