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The goal of the project is to classify an event produced in the particle accelerator as background or signal. 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.

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Higgs Boson Event Detection

Project Report

https://sugatagh.github.io/dsml/projects/higgs-boson-event-detection/

Overview

  • 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.

  • 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.

  • 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.

  • A detailed backdrop of the problem is given, and exploratory data analysis on the provided data is carried out.

  • The observations obtained from EDA are used in the data preprocessing and feature engineering stages.

  • We build a neural network and tune it to predict if a given event is background or signal.

  • 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 $0.824100$.

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The goal of the project is to classify an event produced in the particle accelerator as background or signal. 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.

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