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Transformer networks for Heavy flavor jet tagging
A. Hammad, Mihoko M Nojiri
In this article, we review recent machine learning methods used in challenging particle identification of heavy-boosted particles at high-energy colliders. Our primary focus is on…
Quantum similarity learning for anomaly detection
A. Hammad, Mihoko M. Nojiri, Masahito Yamazaki
Anomaly detection is a vital technique for exploring signatures of new physics Beyond the Standard Model (BSM) at the Large Hadron Collider (LHC). The vast number of collisions gen…
Testing CP properties of the Higgs boson coupling to leptons with heterogeneous graphs
W. Esmail, A. Hammad, M. Nojiri +1
We explore the feasibility of measuring the CP properties of the Higgs boson coupling to leptons at the High Luminosity Large Hadron Collider (HL-LHC). Employing detailed Monte…
Streamlined jet tagging network assisted by jet prong structure
A. Hammad, Mihoko M. Nojiri
Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computati…
Multi-scale cross-attention transformer encoder for event classification
A. Hammad, S. Moretti, M. Nojiri
We deploy an advanced Machine Learning (ML) environment, leveraging a multi-scale cross-attention encoder for event classification, towards the identification of the $gg\to H\to hh…