3 citations · 4 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…
Shedding Light on Dark Matter at the LHC with Machine Learning
Ernesto Arganda, Martín de los Rios, Andres D. Perez +3
We investigate a WIMP dark matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the -symmetric Next-to-Minimal Supersymmetri…
Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
Ernesto Arganda, Marcela Carena, Martín de los Rios +4
The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning…
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…
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…