Applying Normalizing Flows for spin correlations reconstruction in associated top-quark pair and dark matter production
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Abasov, E. Dudko, L. Iudin, E. Markina, A. Volkov, P. Vorotnikov, G. Perfilov, M. Zaborenko, A. |
| author_facet | Abasov, E. Dudko, L. Iudin, E. Markina, A. Volkov, P. Vorotnikov, G. Perfilov, M. Zaborenko, A. |
| contents | We apply a unified machine-learning framework based on Normalizing Flows (NFs) for the event-by-event reconstruction of invisible momenta and the subsequent evaluation of spin-sensitive observables in top-quark pair and dark-matter (DM) associated production processes. Building on recent studies in single-top + DM topologies, we extend the research to $t\bar{t}$ + DM final states. Inputs to our networks combine low-level four-momenta and missing transverse energy with high-level kinematic and angular variables. We compare a baseline multilayer perceptron (MLP) regressor, an autoregressive flow, and the conditional $ν$-Flows model -- trained to learn the full conditional density. In these final states all the models perform well and demonstrate high reconstruction quality in independent regions split by $m_{t\bar{t}}$ for validation purposes. We highlight the potential of this approach to be extended to three- and four-top-quark production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Applying Normalizing Flows for spin correlations reconstruction in associated top-quark pair and dark matter production Abasov, E. Dudko, L. Iudin, E. Markina, A. Volkov, P. Vorotnikov, G. Perfilov, M. Zaborenko, A. High Energy Physics - Phenomenology We apply a unified machine-learning framework based on Normalizing Flows (NFs) for the event-by-event reconstruction of invisible momenta and the subsequent evaluation of spin-sensitive observables in top-quark pair and dark-matter (DM) associated production processes. Building on recent studies in single-top + DM topologies, we extend the research to $t\bar{t}$ + DM final states. Inputs to our networks combine low-level four-momenta and missing transverse energy with high-level kinematic and angular variables. We compare a baseline multilayer perceptron (MLP) regressor, an autoregressive flow, and the conditional $ν$-Flows model -- trained to learn the full conditional density. In these final states all the models perform well and demonstrate high reconstruction quality in independent regions split by $m_{t\bar{t}}$ for validation purposes. We highlight the potential of this approach to be extended to three- and four-top-quark production. |
| title | Applying Normalizing Flows for spin correlations reconstruction in associated top-quark pair and dark matter production |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2510.11644 |