A5092114968- Particle Detector Development and Performance
- Particle physics theoretical and experimental studies
- Computational Physics and Python Applications
- Radiation Detection and Scintillator Technologies
- Neutrino Physics Research
Tel Aviv University
2023-2024
The search for resonant mass bumps in invariant-mass distributions remains a cornerstone strategy uncovering Beyond the Standard Model (BSM) physics at Large Hadron Collider (LHC). Traditional methods often rely on predefined functional forms and exhaustive computational human resources, limiting scope of tested final states selections. This work presents BumpNet, machine learning-based approach leveraging advanced neural network architectures to generalize enhance Data-Directed Paradigm...
Abstract We train several neural networks and boosted decision trees to discriminate fully-hadronic di- τ topologies against background QCD jets, using calorimeter tracking information. Boosted consisting of a pair highly collimated -leptons, arise from the decay energetic Standard Model Higgs or Z boson particles beyond Model. compare tagging performance for different neural-network models tree, latter serving as simple benchmark machine learning model. The code used obtain results...
We train several neural networks and boosted decision trees to discriminate fully-hadronic di-$τ$ topologies against background QCD jets, using calorimeter tracking information. Boosted consisting of a pair highly collimated $τ$-leptons, arise from the decay energetic Standard Model Higgs or Z boson particles beyond Model. compare tagging performance for different neural-network models tree, latter serving as simple benchmark machine learning model.