Overview

Teaching: 0 min
Exercises: 0 min

Questions

• What is machine learning?

• What role does machine learning have in particle physics?

• What should I do if I want to get good at machine learning?

Objectives

• Discuss the general learning task in machine learning.

• Provide examples of machine learning in particle physics.

• Give resources to people who want to become proficient in machine learning.

What is Machine Learning?

General definition

[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed
-Arthur Samuel, 1959

In a traditional approach to solving problems, one would study a problem, write rules (i.e. laws of physics) to solve that problem, analyze errors, then modify the rules. A machine learning approach automates this process: a machine learning model modifies its rules based on the errors it measures. This abstract statement becomes more conceptual once the mathematical foundations are established. For now, you can think of the following example:

• One wishes to fit some data points to a best fit line \(y=ax+b\). One chooses initial “guesses” for \(a\) and \(b\) and a machine learning algorithm finds the optimal values of \(a\) and \(b\) with respect to the mean-squared error.

There are three main tasks in machine learning:

1. Regression. The input is multi-dimensional data points and the output is a real number (or sequence of real numbers). For example, the input might be height and weight and the corresponding output resting heart rate and resting blood pressure (a sequence of real numbers).

2. Classification. The input is multi-dimensional data points and the output is an integer (which represents different classes). Consider the following example with two classes: pictures of roses and pictures of begonias. The input would be multi-dimensional images (color channel included) and one may assign the integer 0 to roses and the integer 1 to begonias.

3. Generation: The input is noise and the output is something sensible. A few concrete examples include training a machine learning algorithm to take in a random seed and generate images of peoples faces.

What Role Does Machine Learning have in Particle Physics?

• One wants to classify detected particles as signal or background events based on their energy, momentum, charge, etc… . This specific problem was featured on Kaggle 6 years ago here. This problem will also be examined in this tutorial.
• (My Research) Particle energy is measured in the liquid argon calotimeter by studying the output current of the electronics. When a particle deposits energy in a cell, a unique pulse shape is formed. One wants to determine the energy of the incident particles based on the form of the current time series. The traditional approach was to use a signal processing technique known as the optimal filter, but as the lumonisity of the LHC increases, these pulse shapes are littered with more and more deposited energy from background events.

Machine learning has become quite popular in scientific fields in the past few years. The following plot from MIT Technical Review examines the of the papers available in the “artificial intelligence” section of arXiv through November 18, 2018.

The following are a few recent articles on machine learning in particle physics

• Machine and Deep Learning Applications in Particle Physics
• Machine learning at the energy and intensity frontiers of particle physics

Where to Become Proficient in Machine Learning

Machine Learning is not something you’ll learn in an hour. It’s a skill you need to develop over time, and like any skill you need to practice a little bit every day. If you want to reall excel at machine learning, my recommendation is to order this book and to read and code along with each chapter. Alternatively, you could also try an online course such as this one. Don’t go crazy: just do 30 minutes a day. You’d be surprised how much you could learn in a couple months. In summary:

• For Learning Essential python libraries: this textbook
• For Learning Machine Learning Libraries: this textbook

Just for a bit of perspective, I started learning python in September 2017 and began learning about machine learning in December 2018. I am finished the first textbook and am still working through the machine learning textbook. Don’t expect the learning process to be a quick one: read each textbook thoroughly and make sure you code along with each textbook.

Key Points

• The 3 main tasks of Machine Learning are regression, classification and generation.

• Machine learning has many applications in particle physics.

• If you want to become proficient in machine learning, you need to practice.