1 citations · 2 across the 5 of their papers we have counts for
5 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…
LHC Study of Third-Generation Scalar Leptoquarks with Machine-Learned Likelihoods
Ernesto Arganda, Daniel A. Díaz, Andres D. Perez +2
We study the impact of machine-learning algorithms on LHC searches for leptoquarks in final states with hadronically decaying tau leptons, multiple -jets, and large missing tran…
Probing new physics with charge asymmetries in 2 same-sign leptons plus jets final states at the LHC
Ernesto Arganda, Leandro Da Rold, Aurelio Juste +2
We study the impact of new physics models in the charge asymmetry defined for LHC final states consisting of two same-sign leptons (2SS, with ) plus jets ($N_\text{jets…
Gradient Boosting MUST taggers for highly-boosted jets
J. A. Aguilar-Saavedra, E. Arganda, F. R. Joaquim +2
The MUST (Mass Unspecific Supervised Tagging) method has proven to be successful in implementing generic jet taggers capable of discriminating various signals over a wide range of…
Machine-Learned Exclusion Limits without Binning
Ernesto Arganda, Andres D. Perez, Martin de los Rios +1
Machine-Learned Likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensi…