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Author (up) Hirsch, M.; Mantani, L.; Sanz, V. url  doi
openurl 
  Title Data-Driven Discovery Strategy for Standard Model Effective Field Theory Searches Type Journal Article
  Year 2025 Publication Physical Review Letters Abbreviated Journal Phys. Rev. Lett.  
  Volume 135 Issue 24 Pages 241801 - 8pp  
  Keywords  
  Abstract We present a novel strategy to uncover indirect signs of new physics in collider data using the standard model effective field theory (SMEFT) framework, offering notably improved sensitivity compared to traditional global analyses. Our approach leverages genetic algorithms to efficiently navigate the high-dimensional space of operator subsets, identifying deformations that improve agreement with data without relying on prior ultraviolet (UV) assumptions. This enables the systematic detection of SMEFT scenarios that outperform the standard model in explaining observed deviations. We validate the approach on current large hadron collider and large electron-positron collider measurements, perform closure tests with injected UV signals, and assess performance under high-luminosity projections. The algorithm successfully recovers relevant operator subsets and highlights directions in parameter space where deviations are most likely to emerge. Our results demonstrate the potential of SMEFT-based discovery searches driven by model selection, providing a scalable framework for future data analyses.  
  Address [Hirsch, Martin; Mantani, Luca; Sanz, Veronica] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, E-46980 Valencia, Spain  
  Corporate Author Thesis  
  Publisher Amer Physical Soc Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0031-9007 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001645065400021 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ pastor @ Serial 6997  
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