244 citations · 713 across the 15 of their papers we have counts for
5 papers · 1 filter
An investigation of over-training within semi-supervised machine learning models in the search for heavy resonances at the LHC
Benjamin Lieberman, Joshua Choma, Salah-Eddine Dahbi +2
In particle physics, semi-supervised machine learning is an attractive option to reduce model dependencies searches beyond the Standard Model. When utilizing semi-supervised techni…
Understanding two same-sign and three leptons with -jets in four top quark events at the LHC
Thuso Mathaha, Abhaya Kumar Swain, Mukesh Kumar +2
The top quark is the heaviest known elementary particle of the Standard Model (SM) of particle physics and, therefore, it is expected to have large couplings to hypothetical new ph…
Unveiling Hidden Physics at the LHC
Oliver Fischer, Bruce Mellado, Stefan Antusch +30
The field of particle physics is at the crossroads. The discovery of a Higgs-like boson completed the Standard Model (SM), but the lacking observation of convincing resonances Beyo…
Accumulating Evidence for the Associated Production of a New Higgs Boson at the Large Hadron Collider
Andreas Crivellin, Yaquan Fang, Oliver Fischer +7
In the last decades, the Standard Model (SM) of particle physics has been extensively tested and confirmed, with the announced discovery of the Higgs boson in 2012 being the last m…
The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC
Thabang Lebese, Xifeng Ruan
Semi-supervision in Machine Learning can be used in searches for new physics where the signal plus background regions are not labelled. This strongly reduces model dependency in th…