Výsledky vyhledávání - "LIEBERMAN, BENJAMIN A."

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Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC

Autor: Lieberman, Benjamin, Dahbi, Salah-Eddine, Crivellin, Andreas, Stevenson, Finn, Tripathi, Nidhi, Kumar, Mukesh, Mellado, Bruce

Publikováno v: SciPost Phys. Core 7, 073 (2024)

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 supervised classifiers these models introduce an additional look-elsewhere ef

Externí odkaz: http://arxiv.org/abs/2404.07822

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COVID-19 South African Vaccine Hesitancy Models Show Boost in Performance Upon Fine-Tuning on M-pox Tweets

Autor: Perikli, Nicholas, Bhattacharya, Srimoy, Ogbuokiri, Blessing, Nia, Zahra Movahedi, Lieberman, Benjamin, Tripathi, Nidhi, Dahbi, Salah-Eddine, Stevenson, Finn, Bragazzi, Nicola, Kong, Jude, Mellado, Bruce

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 into another pandemic, while the COVID-19 pandemic ravages on. Given the

Externí odkaz: http://arxiv.org/abs/2310.04453

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Detecting the Presence of COVID-19 Vaccination Hesitancy from South African Twitter Data Using Machine Learning

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. Vaccination is a major tool in the fight against the pandemic, but vaccine h

Externí odkaz: http://arxiv.org/abs/2307.15072

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Kniha

The Art of Software Modeling. [elektronicky zdroj]

Autor: Lieberman, Benjamin A.

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An investigation of over-training within semi-supervised machine learning models in the search for heavy resonances at the LHC

Autor: Lieberman, Benjamin, Choma, Joshua, Dahbi, Salah-Eddine, Mellado, Bruce, Ruan, Xifeng

In particle physics, semi-supervised machine learning is an attractive option to reduce model dependencies searches beyond the Standard Model. When utilizing semi-supervised techniques in training machine learning models in the search for bosons at t

Externí odkaz: http://arxiv.org/abs/2109.07287

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Machine learning approach for the search of resonances with topological features at the Large Hadron Collider

Autor: Dahbi, Salah-eddine, Choma, Joshua, Mellado, Bruce, Mokgatitswane, Gaogalalwe, Ruan, Xifeng, Lieberman, Benjamin, Celik, Turgay

The observation of resonances is unequivocal evidence of new physics beyond the Standard Model at the Large Hadron Collider (LHC). So far, inclusive and model dependent searches have not provided evidence of new resonances, indicating that these coul

Externí odkaz: http://arxiv.org/abs/2011.09863

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The anomalous production of multi-lepton and its impact on the measurement of $Wh$ production at the LHC

Autor: Hernandez, Yesenia, Kumar, Mukesh, Cornell, Alan S., Dahbi, Salah-Eddine, Fang, Yaquan, Lieberman, Benjamin, Mellado, Bruce, Monnakgotla, Kgomotso, Ruan, Xifeng, Xin, Shuiting

Publikováno v: Eur. Phys. J. C (2021) 81:365

Anomalies in multi-lepton final states at the Large Hadron Collider (LHC) have been reported in Refs.~\cite{vonBuddenbrock:2017gvy,vonBuddenbrock:2019ajh}. These can be interpreted in terms of the production of a heavy boson, $H$, decaying into a Sta

Externí odkaz: http://arxiv.org/abs/1912.00699

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