Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
Submitted to Physics Letters B
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The work supplies a modular SMEFT likelihood for EWPD that includes NLO effects and five input schemes, then combines it with LHC measurements to tighten constraints on lepton-flavor universality and quark coupling asymmetries.
CMS measures the W boson mass as 80360.2 ± 9.9 MeV from 2016 data, consistent with the Standard Model prediction.
Universal SMEFT fits to pseudo-data from neutral and charged Drell-Yan processes at HL-LHC can detect universal new physics and extract its properties stably across EFT truncation orders.
citing papers explorer
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A linear PDF model for Bayesian inference
Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
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An EWPD SMEFT likelihood for the LHC -- and how to improve it with measurements of W and Z boson properties
The work supplies a modular SMEFT likelihood for EWPD that includes NLO effects and five input schemes, then combines it with LHC measurements to tighten constraints on lepton-flavor universality and quark coupling asymmetries.
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High-precision measurement of the W boson mass with the CMS experiment
CMS measures the W boson mass as 80360.2 ± 9.9 MeV from 2016 data, consistent with the Standard Model prediction.
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USMEFT as a tool for discovery of universal new physics at high luminosity LHC
Universal SMEFT fits to pseudo-data from neutral and charged Drell-Yan processes at HL-LHC can detect universal new physics and extract its properties stably across EFT truncation orders.