Decorrelation of neural networks from particle lifetimes in the LHCb topological $b$ trigger
Year: 2026
Type: preprint
Abstract: The LHCb topological beauty trigger is the primary set of algorithms for selecting collision events containing $b$-hadrons in the fully software-based LHCb trigger. The algorithms apply monotonic Lipschitz neural networks (NNs) to select vertices of charged particles consistent with the distinct topology of a $b$ decay, i.e., those with large lifetimes and transverse momentum. Many analyses of the events recorded require that the selection must be unbiased with respect to the $b$-hadron lifetime at large lifetimes. Accurate reconstruction is challenging in busier detector environments, in which several visible proton-proton collisions occur simultaneously per bunch crossing, such that misassociation of decay products can result in vertices with artificially large measured lifetimes. This paper presents two approaches to mitigate correlations between NN scores and candidate lifetimes at large lifetime, and evaluates the performance of the resulting models.
Source: arXiv (Cornell University)
Authors: Albrecht, Johannes, Bertolin, Alessandro, Connaughton, James, Davies, Jonathan, Delaney, Blaise, Dziurda, Agnieszka, Fitzpatrick, Conor, Giza, Maciej, Gligorov, Vava V., Gooding, James A., Schulte, Nicole, Skidmore, Nicole, Vesterinen, Mika, Williams, Mike, Zhang, Shunan, Zhovkovska, Valeriia
Institutions: TU Dortmund University, Istituto Nazionale di Fisica Nucleare, Sezione di Padova, University of Warwick, Department for Work and Pensions, Institute of Nuclear Physics, Polish Academy of Sciences, University of Manchester, Centre National de la Recherche Scientifique, Sorbonne Université, Laboratoire de Physique Nucléaire et de Hautes Énergies, Institut National de Physique Nucléaire et de Physique des Particules, The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Massachusetts Institute of Technology, University of Oxford
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7171403737