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Andrew Levin

Publications and source records attributed to Andrew Levin.

5 recordsLinked to original sources

Constraints on SMEFT operators from $Z \to \mu \mu bb$ decay

The Standard Model Effective Field Theory (SMEFT) provides a systematic framework to probe indirect effects of heavy new physics via precision measurements. While SMEFT constraints have been extensively studied using purely leptonic $Z$ decays and inclusive $Z$ production, mixed leptonic-hadronic modes remain largely unexplored. In this work, we analyze $Z \to \mu\mu bb$ decays within the SMEFT framework, deriving constraints on dimension-six operators that affect four-fermion interactions between leptons and bottom quarks, as well as $Z$-fermion couplings. Signal and background events are simulated with state-of-the-art Monte Carlo tools, including detector effects such as $b$-tagging, and limits on the relevant Wilson coefficients are extracted using kinematic distributions and a profile likelihood approach. Our results provide complementary constraints to existing SMEFT studies and yield the first process-specific limits on flavor-resolved four-fermion operators involving muons and bottom quarks from $Z$ decays.

hep-ph

Prospects for measuring electroweak production of $Z\gamma\gamma$ and 2 jets at the LHC

Vector boson scattering (VBS) serves as a powerful channel for probing the Standard Model, particularly the electroweak symmetry breaking mechanism. Currently, studies of VBS mainly focus on $2 \to 2$ scattering. In this study, we investigate the $2\to 3$ VBS process of $\text{p p} \to Z\gamma\gamma + 2~\text{jets}$ through Monte Carlo simulations, including signal generation and background analysis. The signal significance is evaluated across different phase-space regions. With an integrated luminosity of 500 fb$^{-1}$, the signal significance can reach about 4.5 $\sigma$. These results suggest that, given the ongoing release of the LHC Run 3 dataset, there will be promising opportunities to explore and potentially discover a series of $2\to 3$ VBS processes, starting with the $Z\gamma\gamma$ channel.

hep-ph

Quantum Entanglement between gauge boson pairs at a Muon Collider

Quantum entanglement is one of significant physics phenomena that can be examined at a particle collider. A muon collider can provide a stage on which we can study substantial physics phenomenon, starting from the precision measurements of the Standard Model and beyond to the undiscovered area of physics. In this work, we present a through study of quantum entanglement in $\mu^+\mu^-\to ZZ$ events at a future muon collider. By fixing the spin density matrix, observables quantifying entanglement between $Z$ boson pairs can be measured. After systematic Monte-Carlo simulation and background analysis, we measure the value of entanglement variables and perform hypothesis testing against the non-entangled hypothesis, finally observing the entanglement of the $ZZ$ system up to $2$ significance level.

hep-ph

Polarization fraction measurement in ZZ scattering using deep learning

Measuring longitudinally polarized vector boson scattering in the ZZ channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible new physics. We investigated several deep neural network structures and compared their ability to improve the measurement of the longitudinal fraction Z_L Z_L. Using fast simulation with the Delphes framework, a clear improvement is found using a previously investigated 'particle-based' deep neural network on a preprocessed dataset and applying principle component analysis to the outputs.A significance of around 1.7 standard deviations can be achieved with the integrated luminosity of 3000 fb-1 that will be recorded at the High-Luminosity LHC.

hep-ph

Polarization fraction measurement in same-sign WW scattering using deep learning

Studying the longitudinally polarized fraction of $W^\pm W^\pm$ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and possible new physics. We apply here for the first time a Deep Neural Network classification to extract the longitudinal fraction. Based on fast simulation implemented with the Delphes framework, significant improvement from a deep neural network is found to be achievable and robust over all dijet mass region. A conservative estimation shows that a high significance of four standard deviations can be reached with the High-Luminosity LHC designed luminosity of 3000 $fb^{-1}$

hep-ph