In this article, we review the application of modern machine-learning (ML) techniques to boost the search for processes involving the top quarks at the LHC. We revisit the formalism of Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Attention Mechanisms. Based on recent studies, we explore their applications in designing improved top taggers, top reconstruction, and event classification tasks. We also examine the ML-based likelihood-free inference approach and generative unfolding models, focusing on their applications to scenarios involving top quarks.
@article{arxiv.2407.00183,
title = {Top-philic Machine Learning},
author = {Rahool Kumar Barman and Sumit Biswas},
journal= {arXiv preprint arXiv:2407.00183},
year = {2024}
}
Comments
A short review prepared by invitation for EPJ Special Topics issue. Version accepted for publication; 45 pages, 17 figures, 1 table; v2: typos corrected