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name: Topological Rare Hadron Decay Tagging with DNN | ||
postdate: 2024-04-15 | ||
categories: | ||
- Analysis tools | ||
durations: | ||
- 3 months | ||
experiments: | ||
- CMS | ||
skillset: | ||
- Python | ||
- ML | ||
- Linux | ||
- Git | ||
status: | ||
- Available | ||
project: | ||
- IRIS-HEP | ||
location: | ||
- Remote | ||
commitment: | ||
- Full time | ||
program: | ||
- IRIS-HEP fellow | ||
shortdescription: Deep neural net topological tagger for rare hadron decay identification | ||
description: > | ||
The CMS experiment provides a unique opportunity to study extremely rare hadron | ||
decays due to a significantly larger amount of data compared with the LHCb and | ||
Belle II experiments. The main challenge is to identify such decays and suppress | ||
associated backgrounds in the high pile-up environment. In this project, we will | ||
use a Deep Neural Network to develop a new method of tagging such decays using | ||
topological information to identify the relevant charged tracks associated with | ||
the decays of interest. The project requires familiarity with machine learning | ||
algorithms and tools to build and train deep neural networks. It will involve | ||
testing different DNN types and feature engineering. | ||
contacts: | ||
- name: Dmytro Kovalskyi | ||
email: [email protected] | ||
mentees: |