Machine Learning is having a major impact in jet physics. It is empowering powerful taggers for boosted (W, Z, H, top) jets as well as flavor tagging. NYU will host ML4Jets 2020 focusing on this topic. This will be the third in the series of workshops, following ML4Jets 2018 at FermiLab and ML4Jets 2017 at Berkeley.
At ML4Jets 2018 a top-tagging comparison was initaited, resulting in ``The Machine Learning Landscape of Top Taggers’’. The table below shows a comparison of the methods.
The QCD-aware recursive neural networks devleoped as part of DIANA/HEP (Louppe et al. 2017) which leverage an analogy to natural language processing were extended to include a network-in-network. The TreeNiN method (in the table above), achieves excellent performance with orders of magnitude fewer parameters than the other top performing techniques. This pytorch implementation can be found in this repository
Irina Espejo, Sebastian Macaluso, and Heiko Mueller are using docker containers, yadage workflows, and REANA to automate and streamline such benchmark studies.
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Team
- Kyle Cranmer
- Sebastian Macaluso
- Irina Espejo
Publications
- Hamiltonian Graph Networks with ODE Integrators, A. Sanchez-Gonzalez, V. Bapst, K. Cranmer and P. Battaglia, arXiv 1909.12790 (27 Sep 2019).
- The Machine Learning Landscape of Top Taggers, G. Kasieczka, T. Plehn, A. Butter, K. Cranmer, D. Debnath et. al., SciPost Phys. 7 014 (2019) (26 Feb 2019).
- QCD-Aware Recursive Neural Networks for Jet Physics, G. Louppe, K. Cho, C. Becot and K. Cranmer, JHEP 01 057 (2019) (02 Feb 2017).