Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine-learning approaches are often used. The primary tools of experimental high-energy physicists are modern accelerators, which collide protons and/or antiprotons to create exotic particles that occur only at extremely high-energy densities. Observing these particles and measuring their properties may yield critical insights about the very nature of matter. Such discoveries require powerful statistical methods, and machine-learning tools have a critical role. Given the limited quantity and expensive nature of the data, improvements in analytical tools directly boost particle discovery potential.
The high dimensionality of data, referred to as the feature space, makes it intractable to generate enough simulated collisions to describe the relative likelihood in the full feature space, and machine-learning tools are used for dimensionality reduction. Machine-learning classifiers such as Decision Trees and Ensembles of Decision Trees provide a powerful way to solve this learning problem.
This assignment focuses on solving the problem of classification of measurement data from particle accelerators to detect the presence of a fundamental particle, where the algorithm needs to classify the problem as either Made By particle or Background Noise.
FURTHER READING: Baldi, P., P. Sadowski, and D. Whiteson. “Searching for Exotic Particles in High-energy Physics with Deep Learning.” Nature Communications 5 (July 2, 2014)