Outreach

Building and Characterising DIY Particle Detectors

Student researchers: Eric Ji, Bogdan Pamfile, Jovitta Winceslaus, Jessica Yu
Supervisor: Prof. Yoshi Uchida

In this project, we built several DIY particle detectors, including a cloud chamber, scintillator and photodiode-based detector, with mostly commercially available products, to detect and characterise common particles such as cosmic muons and beta particles. We measured the count rates of muons using all the particle detectors, and compared the efficiencies of their detection. We also investigated the energy spectra of muons detected with the photodiode-based detector and scintillator, and verified the relationship between muon flux and zenith angle with the Geiger counters-based detector. The video is broken down into 3 key parts, theory, project work, and results and discussion, which developed on the ideas above.

Modelling Gravitational Lensing and Einstein Rings, Analysis of SLOAN Lens Survey Data set

Student researchers: Joseph Dyson, Prabhmeet Johal, Leiyi Sun, Cyrus Tang
Supervisor: Elias van den Dreissche

Using python our team developed a ray tracing model of gravitational lensing to measure the theoretical value of the radius of an Einstein ring around a lens and compared the theoretical prediction to those recorded in the Sloan lens ACS survey (SLACS). We analysed the data to investigate any discrepancies between theoretical and observational data and possible reasons that could explain these differences. We also created an image of a black hole, and analysed the data to show that dark matter exists.

Selective Hearing: Combining Sound Source Localisation and Active Noise Cancellation

Student researchers: Abram Chui Hoi Yiu, Mei Kojima, Robert Lascar, Aanya Maheshwari
Supervisor: Dr Mark Richards

A small-scale active noise cancellation and direction estimation prototype was developed using a RaspberryPi, I2S microphones and a headphone model. Direction estimation was achieved using a GCC-PHAT beamforming algorithm, while ANC was tested using adaptive filtering. The system showed successful angle detection and achieved a maximum signal correlation of 93% at 170 Hz, although latency and hardware limitations prevented full simultaneous directional ANC.

Can Physics Win the World Cup?: The Aerodynamics Behind Football's Most Chaotic Freekicks

Student researchers: Abu-Bakr Camatchee, Bihadu Jayaweera, Sharen Shivagumar, Reilly Webster
Supervisor: Prof. Jonathan Eastwood

An investigation into the physics behind knuckleball free-kicks in football using research papers has been completed in light of the upcoming World Cup, identifying two main aspects to a knuckleball. Using existing video footage one of the two main aspects were verified. A simulation was produced based on wind tunnel data to verify the other aspect. By combining all aspects of the investigation a game was produced based upon the physics to show the random chaotic nature of a knuckleball. Finally, it was determined that a knuckleball free-kick should not routinely be attempted at the 2026 World Cup.