Author(s)
How to Obtain Support
General Information
| ML/DL Technologies | Statistical Learning; Forward Neural Networks |
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| Science Fields | High Energy Physics |
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| Difficulty | Intermediate |
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| Language | English |
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| Type | fully annotated |
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Software and Tools
| Programming Language | Python |
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| ML Toolset | scikit-learn; Keras + Tensorflow |
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| Additional libraries | uproot |
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| Suggested Environments | INFN-Cloud VM, bare Linux Node, Google CoLab |
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Needed datasets
| Data Creator | CMS Experiment |
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| Data Type | Simulation |
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| Data Size | < 1 GB |
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| Data Source | Google Drive |
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Short Description of the Use Case
Classification is a very common problem in High Energy Physics and historically it has been one of the first to be addressed with machine learning techniques.
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This is the starting point for most statistical analyses in High Energy Physics: one has to develop an algorithm to classify signal and background events using simulation. That algorithm will then be run on real data and the resulting selected events have to be statistically subtracted for the expected contribution from background events in order to count the number of signal events and use them to infer physical properties of the decay.
How to execute it
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