https://sugatagh.github.io/dsml/projects/higgs-boson-event-detection/
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In particle physics, an event refers to the results just after a fundamental interaction took place between subatomic particles, occurring in a very short time span, at a well-localized region of space.
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A background event is explained by the existing theories and previous observations. On the other hand, a signal event indicates a process that cannot be described by previous observations and leads to the potential discovery of a new particle.
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In this project, we aim to predict if a given event is background or signal, based on the data provided in the Kaggle competition Higgs Boson Machine Learning Challenge.
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A detailed backdrop of the problem is given, and exploratory data analysis on the provided data is carried out.
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The observations obtained from EDA are used in the data preprocessing and feature engineering stages.
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We build a neural network and tune it to predict if a given event is background or signal.
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We employ the approximate median significance (AMS) metric to evaluate the models. The final model obtains a training AMS of
$2.500144$ and a test AMS of$1.200022$ . It achieves a training accuracy of$0.827070$ and a test accuracy of$0.824100$ .