Introduction to Machine Learning in HEP

Last updated on 2024-07-19 | Edit this page

Overview

Questions

How can machine learning be applied to particle physics data?
What are the steps involved in preparing data for machine learning analysis?
How do we train and evaluate a machine learning model in this context?

Objectives

Learn the basics of machine learning and its applications in particle physics.
Understand the process of preparing data for machine learning.
Gain practical experience in training and evaluating a machine learning model.

Overview

Machine learning (ML) is a powerful tool for extracting insights from complex datasets, making it invaluable in high-energy physics (HEP) research. The Machine Learning in High-Energy Physics (HEP) activity bridges the gap between data science and particle physics, utilizing CMS Open Data to explore real-world applications. Participants will learn to leverage ML algorithms to analyze particle collision data, enabling them to classify events, discover new particles, and enhance their understanding of fundamental physics.

Prerequisites

Before diving into ML in HEP, participants should have a basic understanding of: - Programming fundamentals (Python recommended) - Data handling and visualization - Elementary statistical concepts (mean, variance, etc.)

Let’s get the basics clear

Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. If that is not clear, please watch this video.

Artificial Intelligence (AI) is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Machine Learning (ML) is a subset of AI that involves training algorithms to learn from and make predictions based on data.

Callout

Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.

To have an overview of neural networks, visit 3Blue1Brown’s basics of neural networks, and the math behind how they learn.

Data Acquisition and Understanding

By now we must have a basic understanding of how Machine Learning functions, to use this in the realm of High Energy Physics, we must have the following basics.

CMS Open Data Overview: - Accessing and understanding the CMS Open Data repository. - Types of datasets available (e.g., AOD, MiniAOD, NanoAOD) and their differences. - Introduction to the CMS experiment and its detectors.

Data Preparation - Cleaning and Preprocessing

As you dive into the hackathon, keep in mind that feature engineering—like selecting relevant features, creating new ones to enhance model performance, and using dimensionality reduction techniques play a crucial role in both supervised and unsupervised learning. Mastering these techniques will significantly impact your models’ ability to learn from and make sense of your data, so be sure to leverage them effectively in your projects!

Supervised learning involves training a model on labeled data, where the input comes with corresponding output labels, allowing the model to learn the relationship between inputs and outputs. In contrast, unsupervised learning works with unlabeled data, identifying patterns and structures within the data without predefined labels, often used for clustering and association tasks.

You can get a glimpse of the differences in this video.

Supervised Learning in HEP

Basics of Supervised Learning

Understanding labeled datasets and target variables.
Classification tasks: distinguishing particle types (e.g., muons, electrons).
Regression tasks: a possible application in HEP can be predicting particle properties (e.g., energy, momentum).

Model Selection and Training

Choosing appropriate algorithms (e.g., Decision Trees, Random Forests, Neural Networks).
Cross-validation techniques to optimize model performance.
Hyperparameter tuning to fine-tune model behavior.

Model Evaluation

Metrics: accuracy, precision, recall, F1-score.
Confusion matrices and ROC curves for performance visualization.
Interpreting results and refining models based on feedback: Watch this video for Learning Curves In Machine Learning explanation.

Confusion metrics, also known as a confusion matrix, is a table used to evaluate the performance of a classification model. It displays the true positives, true negatives, false positives, and false negatives, providing insight into the accuracy, precision, recall, and overall effectiveness of the model’s predictions.

Unsupervised Learning in HEP

Basics of Unsupervised Learning

Clustering algorithms (K-means, DBSCAN) for grouping similar events.
Anomaly detection techniques to identify unusual data points.
Dimensionality reduction methods (PCA, LDA) for visualizing high-dimensional data.

Applications in Particle Physics

Discovering new particles through anomaly detection.
Grouping events based on similar characteristics (clustering).
Simplifying complex datasets for further analysis.

Conclusion

The Machine Learning with Open Data lesson equips participants with fundamental skills to apply ML techniques effectively in high-energy physics research. By mastering data preparation, model training, and evaluation, participants gain insights into both machine learning principles and their practical applications in particle physics.

Key Points

Introduction to machine learning in particle physics.
Comprehensive data preparation for machine learning analysis.
Supervised and unsupervised learning techniques specific to HEP.
Advanced ML applications in particle physics research.