5 papers
Articulating Assumptions in AI-Generated Scientific Analyses through Task Decomposition
Ahmed Hammad, Mihoko Nojiri
Scientific results produced by LLM generated analysis code must be understandable and reproducible. However, uncertainty can arise at different stages of the process, both in the o…
IAFormer: Interaction-Aware Transformer network for collider data analysis
W. Esmail, A. Hammad, M. Nojiri
In this paper, we introduce \texttt{IAFormer}, a novel Transformer-based architecture that efficiently integrates pairwise particle interactions through a dynamic sparse attention…
CoLLM: AI engineering toolbox for end-to-end deep learning in collider analyses
W. Esmail, A. Hammad, M. Nojiri
Recent improvements in large language models have opened new opportunities for accelerating and automating scientific workflows. In parallel, modern collider analyses are becoming…
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…