The picture above shows germanium detectors that are inside the Majorana Demonstrator experiment. The main goal of this experiment is to search for neutrinoless double-beta decay and test whether neutrinos are their own anti-particle.

Our group has several germanium detectors and focuses on understanding the detailed properties of how electric charges create signals in these detectors. We also work with novel electronics to test different ways to instrument such detectors for the next generation experiment that will use germanium detectors, LEGEND.

We also develop a number of machine learning tools to process the data from these detectors, in particular to remove electronic noise from signals and characterize them precisely.

The above picture shows the installation of the NEWS-G experiment at SNOLAB. This experiment consists in a large (1.4m diameter) sphere filled with gas designed to detect Dark Matter.

Our group has developed much of the software for the analysis framework. We also develop machine-learning tools to process data collected from the experiment that allow us to extract important information. For example, by looking at the pulses recorded by the detector, we can infer the number of gas atoms that were ionized as the result of a Dark Matter particle interacting in the detector.

The above picture shows the SNO+ experiment under construction. The experiment is located 2km underground at SNOLAB, and consists of a 12m diameter spherical acryllic vessel that is filled with liquid scintillator and instrumented with almost 10,000 very sensitive light detectors (photomultiplier tubes).

The experiment will allow us to learn about neutrinos that come from the Sun, neutrinos produced deep in the Earth, and neutrinos produced in nuclear reactors. The experiment will ultimately be used to search for the existence of neutrinoless double-beta decay and test the hypothesis that neutrinos are Majorana particles (namely, that they are their own anti-particle).

Our group focuses on developing machine-learning based algorithms to help reconstructing the position, direction and energy of events that occur in SNO+. This allows us to better separate the faint signals that are expected from neutrino events.

The figure above shows output from the Queen’s University Agent-Based Code for modeling epidemics (figure by A. Micuda). The code is developed by a team of primarily undergraduate students. Our CV-19 modelling group has students from all years, as well as graduate students that provide mentoring and review the work. We accept any interested student into the group!

This group has been an ideal place for students to learn about different aspects of research, including literature research, computer modelling and analysis. We have students from physics, engineering (physics and applied math), bio-mathematics, and life sciences. For example, one student came from life science with no programming experience and was able to learn Python and include vaccinations into the code.

Students in the group have also has the opportunity to learn about the funding cycle in research and have applied to receive over $10,000 in funding to pay stipends to themselves as well as attend several conferences, where they have won prizes for their work.

The focus of the group is to develop models to better understand the pandemic and to inform public health. As of Summer 2022, we are working on our first scientific publication. In the future, we plan for any undergraduate of the group that is interested to be able to lead the work to write a first author publication that makes use of the code.  Get in touch with us if you are interested in learning some new research skills by joining this group!