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The Higgs Boson particle was discovered in 2013. A key property of any particle is how often it decays into other particles. ATLAS is a particle physics experiment taking place at the Large Hadron Collider at CERN that searches for new particles and processes using head-on collisions of protons of extraordinarily high energy. The ATLAS experiment has recently observed a signal of the Higgs boson decaying into two tau particles, but this decay is a small signal buried in background noise.
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The goal is to improve the procedure that produces the selection region. We provide a training set with signal/background labels and with weights, a test set (without labels and weights), and a formal objective representing an approximation of the median significance (AMS) of the counting test.
- The objective of the project is to classify an event produced in the particle accelerator as background or signal. As described in the report of the dataset on Kaggle, a background event is explained by the existing theories and previous observations. A signal event, however, indicates a process that cannot be described by previous observations and leads to the potential discovery of a new particle.
The dataset from Kaggle has 800000 events (195.5 MB in total):
* Training set of 250000 events
* Test set of 550000 events
Training set has 30 feature columns, a weight column and a label column. Test set has 30 feature columns and a label column.
This project has the following flow:
- Preprocessing and Cleaning of the data
- Base Model:
- Ensemble Models:
- Experimentation:
- Interpretation:
- Post modeling, interpret your results.
- Conclusions: