74 citations · 958 across the 59 of their papers we have counts for
60 papers
Two-Stage Machine Learning Strategy for Scalar and Vector Leptoquark Discrimination at the LHC
Ernesto Arganda, Martín de los Rios, Andres D. Perez +2
We present a machine learning framework for the characterization of leptoquark (LQ) signals at the Large Hadron Collider, focusing on the discrimination between scalar (SLQ) and ve…
Exploring enhanced non-resonant di-Higgs production at the HL-LHC with neural networks
Leandro Da Rold, Manuel Epele, Anibal D. Medina +2
We investigate di-Higgs production in the final state at the LHC, focusing on scenarios where the gluon fusion process is enhanced by new colored scalars, which could…
Di-Higgs to 4b with Bayesian inference: improving simulation estimates
Ezequiel Alvarez, Leandro Da Rold, Manuel Szewc +3
Measuring di-Higgs production in the four-bottom channel is challenged by overwhelming QCD backgrounds and imperfect simulations. We develop a Bayesian mixture model that simultane…
Inferring correlated distributions: boosted top jets
Ezequiel Alvarez, Manuel Szewc, Alejandro Szynkman +2
Improving the understanding of signal and background distributions in signal-region is a valuable key to enhance any analysis in collider physics. This is usually a difficult task…
Probing triple-gauge couplings in anomalous gauge theories at hadron and lepton colliders
Anibal D. Medina, Nicolás I. Mileo, Alejandro Szynkman +3
Gauge anomalous quantum field theories are inconsistent as full UV theories since they lead to the breaking of Lorentz invariance or Unitarity, as well as non-renormalizability. It…
Tripartite entanglement from experimental data: as a case study
Roberto A. Morales, Alejandro Szynkman
We develop an angular analysis based on the reconstruction of the helicity amplitudes from dedicated experimental data corresponding to the tripartite state composed by one qutrit…