Learning latent jet structure
We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Alloca...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
29 June 2021
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| In: |
Symmetry
Year: 2021, Jahrgang: 13, Heft: 7, Pages: 1-11 |
| ISSN: | 2073-8994 |
| DOI: | 10.3390/sym13071167 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3390/sym13071167 Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/2073-8994/13/7/1167 
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| Verfasserangaben: | Barry M. Dillon, Darius A. Faroughy, Jernej F. Kamenik and Manuel Szewc |
| Zusammenfassung: | We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation. Using a mixed sample of QCD and boosted tt¯ jet events and focusing on the primary Lund plane observable basis for event measurements, we show how using educated priors on the latent distributions allows to infer on the underlying physical processes in a semi-supervised way. |
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| Beschreibung: | Gesehen am 14.10.2021 |
| Beschreibung: | Online Resource |
| ISSN: | 2073-8994 |
| DOI: | 10.3390/sym13071167 |