Machine Learning for Discovery in Particle Physics
What are Dark Matter and Dark Energy? Why is our universe only made of matter when there were equal amounts of matter and antimatter involved in the big bang? Why are some fundamental particles, like the top quark which weighs almost as much as a gold nucleus, so heavy while others, like neutrinos so light that we haven't even managed to measure their mass yet? These are some of the big, open questions facing particle physics. At the CERN lab, in Geneva, Switzerland, the Large Hadron Collider (LHC) collides protons together at the highest energies ever achieved in a laboratory, searching for signs of new particle or new interactions that would help us answer those questions. Notre Dame participates in the Compact Muon Solenoid (CMS) experiment, that collects data from these LHC collisions. This research project involves analyzing CMS data to search for signs of new particles or interactions.
While the answers we seek require new particles and/or interactions, there is no guarantee that those particles exist within the energy reach of the LHC. The LHC converts the kinetic energy of accelerated protons into new particles, using Einstein's famous E=mc^2 equation. However, particles whose mass exceeds the available proton kinetic energy in the collisions cannot be produced directly. Because it will be a long time before we have a more powerful particle accelerator, my group is using a technique called Effective Field Theory (EFT) to search for indirect signs of very heavy particles might have on the LHC data. EFT allows us to predict how different types of hypothetical super-massive particles might impact the CMS data, and then search for those effects.
Because the indirect effects we're looking for may be small, and because the data is complex and rich, we need to turn to advanced analysis techniques to maximize our chances for discovery. The most powerful technique available is known as machine learning, when our computational algorithm learns for examples of simulated data how to spot the interesting collisions from among all the uninteresting ones. The specific tool we use is called a deep neural network.
In this project you will analyze CMS data, looking for ways to improve sensitivity to EFT effects through the application of deep neural networks. There may also be the opportunity to work on machine learning techniques to enhance the statistical analysis or visualization of high dimensional data.
Name of research group, project, or lab
The Lannon Group
Why join this research group or lab?
In joining the Lannon Group, you will have the opportunity to become part of a team of postdoctoral researchers, graduate students, and other undergraduate students who apply advanced data analysis techniques to try to tease out nature's secrets from some of the most complex data ever collected in a scientific experiment. You will have the opportunity to interact with the international CMS collaboration, networking with researchers working on the CMS experiment around the globe. You will have the chance to work with cutting edge data science and machine learning tools. Your efforts will help to advance humanities understanding of the fundamental laws of nature that govern our universe. It is highly unlikely, at any point during this research, that you would fall through the event horizon of a black hole and be lost from the space-time continuum forever.
