# 4th Inter-experiment Machine Learning Workshop

Oct 19 – 23, 2020
Europe/Zurich timezone

## Disentangling Boosted Higgs Boson Production Modes with Machine Learning

Oct 22, 2020, 2:40 PM
5m
Lightning talk 2 ML for analysis : Application of Machine Learning to analysis, event classification and fundamental parameters inference

### Speaker

Yi-Lun Chung (National Tsing Hua University (TW))

### Description

Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum ($p_T$) are sensitive probes of physics beyond the Standard Model. However, high $p_T$ Higgs Boson production is contaminated by a diversity of production modes other than ggF: vector boson fusion, production of a Higgs boson in association with a vector boson, and production of a Higgs boson with a top-quark pair. Combining jet substructure and event information with modern machine learning, we demonstrate the ability to focus on particular production modes. These tools hold great discovery potential for boosted Higgs bosons produced via ggF and may also provide additional information about the Higgs Boson sector of the Standard Model in extreme phase space regions for other production modes as well.

### Primary authors

Yi-Lun Chung (National Tsing Hua University (TW)) Prof. Shih-Chieh Hsu (University of Washington Seattle (US)) Dr Benjamin Nachman (Lawrence Berkeley National Laboratory)

### Presentation materials

 Disentangling Boosted Higgs Boson-IML.pdf IML2020_thupm_chung.mp4