期刊
SCIPOST PHYSICS
卷 9, 期 5, 页码 -出版社
SCIPOST FOUNDATION
DOI: 10.21468/SciPostPhys.9.5.074
关键词
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资金
- IMPRS-PTFS
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [396021762 - TRR 257]
- DFG under Germany's Excellence Strategy [EXC 2121, 390833306]
For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.
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