Linear Digressions

In the 1850s, Dr. John Snow solved the mystery of a cholera outbreak in London by tracing the geography of deaths and discovering an outlier. His findings validated the germ theory of disease and revolutionized our understanding of disease transmission.

Machine learning and particle physics go together like peanut butter and jelly--but this is a relatively new development. For many decades, physicists looked through their fairly large datasets using the laws of physics to guide their exploration; that tradition continues today, but as ever-larger datasets get made, machine learning becomes a more tractable way to deal with the deluge. With this in mind, ATLAS (one of the major experiments at CERN, the European Center for Nuclear Research and home laboratory of the recently discovered Higgs boson) ran a machine learning contest over the summer, to see what advances could be found from opening up the dataset to non-physicists. The results were impressive--physicists are smart folks, but there’s clearly lots of advances yet to make as machine learning and physics learn from one another. And who knows--maybe more Nobel prizes to win as well! https://www.kaggle.com/c/higgs-boson