1 citations · 1 across the 3 of their papers we have counts for
5 papers
Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC
Benjamin Lieberman, Salah-Eddine Dahbi, Andreas Crivellin +4
To mitigate the model dependencies of searches for new narrow resonances at the Large Hadron Collider (LHC), semi-supervised Neural Networks (NNs) can be used. Unlike fully supervi…
COVID-19 South African Vaccine Hesitancy Models Show Boost in Performance Upon Fine-Tuning on M-pox Tweets
Nicholas Perikli, Srimoy Bhattacharya, Blessing Ogbuokiri +8
Very large numbers of M-pox cases have, since the start of May 2022, been reported in non-endemic countries leading many to fear that the M-pox Outbreak would rapidly transition in…
Detecting the Presence of COVID-19 Vaccination Hesitancy from South African Twitter Data Using Machine Learning
Nicholas Perikli, Srimoy Bhattacharya, Blessing Ogbuokiri +8
Very few social media studies have been done on South African user-generated content during the COVID-19 pandemic and even fewer using hand-labelling over automated methods. Vaccin…
Growing Excesses of New Scalars at the Electroweak Scale
Srimoy Bhattacharya, Guglielmo Coloretti, Andreas Crivellin +4
We combine searches for scalar resonances at the electroweak scale performed by the Large Hadron Collider experiments ATLAS and CMS where persisted excesses have been observed in r…
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…