Janssen, Timo

[["dc.bibliographiccitation.artnumber","678"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","The European Physical Journal C"],["dc.bibliographiccitation.volume","82"],["dc.contributor.author","Yallup, David"],["dc.contributor.author","Janßen, Timo"],["dc.contributor.author","Schumann, Steffen"],["dc.contributor.author","Handley, Will"],["dc.date.accessioned","2022-08-16T12:58:28Z"],["dc.date.available","2022-08-16T12:58:28Z"],["dc.date.issued","2022-08-05"],["dc.date.updated","2022-08-07T03:11:41Z"],["dc.description.abstract","Abstract\r\n We present the first application of a Nested Sampling algorithm to explore the high-dimensional phase space of particle collision events. We describe the adaptation of the algorithm, designed to perform Bayesian inference computations, to the integration of partonic scattering cross sections and the generation of individual events distributed according to the corresponding squared matrix element. As a first concrete example we consider gluon scattering processes into 3-, 4- and 5-gluon final states and compare the performance with established sampling techniques. Starting from a flat prior distribution Nested Sampling outperforms the Vegas algorithm and achieves results comparable to a dedicated multi-channel importance sampler. We outline possible approaches to combine Nested Sampling with non-flat prior distributions to further reduce the variance of integral estimates and to increase unweighting efficiencies."],["dc.identifier.citation","The European Physical Journal C. 2022 Aug 05;82(8):678"],["dc.identifier.doi","10.1140/epjc/s10052-022-10632-2"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112752"],["dc.language.iso","en"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.title","Exploring phase space with nested sampling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","SciPost physics"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Bothmann, Enrico"],["dc.contributor.author","Janssen, Timo"],["dc.contributor.author","Knobbe, Max"],["dc.contributor.author","Schmale, Tobias"],["dc.contributor.author","Schumann, Steffen"],["dc.date.accessioned","2020-01-31T10:41:30Z"],["dc.date.available","2020-01-31T10:41:30Z"],["dc.date.issued","2020"],["dc.description.abstract","We present a novel approach for the integration of scattering cross sections and the generation of partonic event samples in high-energy physics. We propose an importance sampling technique capable of overcoming typical deficiencies of existing approaches by incorporating neural networks. The method guarantees full phase space coverage and the exact reproduction of the desired target distribution, in our case given by the squared transition matrix element. We study the performance of the algorithm for a few representative examples, including top-quark pair production and gluon scattering into three- and four-gluon final states."],["dc.format.extent","19"],["dc.identifier.arxiv","2001.05478"],["dc.identifier.doi","10.21468/SciPostPhys.8.4.069"],["dc.identifier.eissn","2542-4653"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62917"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Exploring phase space with Neural Importance Sampling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]