How Machine Learning is transforming the field of Particle Physics
Particle physics is a branch of physics that studies the fundamental constituents of matter and radiation, and the interaction between them. The study involves using particle accelerators which uses Electric field to accelerate charged particles, which are then smashed onto a target or a particle circulating in the opposite direction. Studying these collisions gives us insights into the working of our world at the most fundamental level.
On the other hand, Machine Learning(ML) is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-life interactions.
So, what these two seemingly different subjects can have anything in common with each other?
If you guessed Data, then congratulations, you are on your way to CERN :)
Jokes aside, CERN produces huge amount of data every day. According to the CERN official website, CERN produces 1 Petabyte (1 million Gigabytes) of Data every day. What to do with this vast amount of data?
We have to process the data to derive meaningful conclusions out of this vast amount of data generated. That is where ML Algorithms step in.
From Detector stimulation to particle reconstruction, tagging etc., ML has played a massive role in the development of the field.
The Standard Model of Particle Physics which describes the various interactions among different fundamental particles (except the gravitational interaction) requires trillions of stimulated collisions in order to achieve the statistical accuracy to validate a hypothesis of the theory. This requires huge computational power which is eased by the use of ML algorithms.
Many of the particles produced in collisions in the particle detectors are extremely short lived (for example the higgs boson decays at approximately 10–22 seconds) thus providing very less time to directly observe the particles. Instead, the decay products of the initial particle can be used to infer the properties. ML algorithms are employed to reconstruct such short-lived particles.
Another fascinating field is Deep Learning(DL), a subfield of ML which involves having layered structures which can be utilized to learn from the data and make conclusions from the data continuously improving upon it just like a human brain would do. (For example — Artificial Neural Networks(ANN)) This provides more efficient ways of performing various tasks like particle reconstructions etc. with more efficient algorithms and with more sensitivity in measurements.
ML and DL are playing a very important role in the modern world, transforming the way we look at the world, bringing us technologies like self-driving cars, Fraud Detection, services in Healthcare etc. Evidently, with vast pool of research that is going on in this field, it has made its way into particle physics too and transforming the way that particle physics experiments were performed.
But it is also important to realize that this is a two-way street. Insights from physics can help to understand better how ML and DL operate, and potentially to design better network architectures further advancing the field.
Overall, if you have a curiosity to understand how the world works at a fundamental level and what are the fundamental particles that it is made up of, particle physics is THE field for you.
References
1. Machine Learning in High Energy Physics Community white paper
https://indico.cern.ch/event/567550/papers/2656686/files/6959-HEPML_CWP_ACAT.pdf