Radiation Detection for the 2010 Olympics
When a country hosts the Winter Olympics, someone has to make sure the radiation detectors work, the data arrives in real time, and the numbers can be trusted.
Engineering record. Delivered by Jonathan Lam, Lamalo’s lead engineer, as a research assistant at Health Canada (2008–2009), and later as a consultant under a Defence Research and Development Canada contract — before the studio was founded.
- Detectors
- 50+
- Real-time data
- Vancouver 2010
- Simulation
- GEANT4
- Research
- Peer-reviewed
The challenge
Radiation monitoring is only as good as its weakest detector and its slowest data path. More than 50 nuclear radiation detectors needed working hardware and software, their telemetry had to be sorted and ingested quickly enough for analysis and reporting, and the instruments themselves had room to become more efficient.
From detector to report
- Detect50+ detectors
Hardware and software developed and debugged, unit by unit.
- AcquireReal time
Data acquisition supported during the 2010 Vancouver Winter Olympics.
- IngestC++ / VB pipeline
Large volumes of telemetry sorted and ingested for analysis.
- ReportAnalytics
Clean data feeding analysis and reporting.
How it was done
- Hands-on hardware and software for more than 50 detectors — building, testing and debugging until every unit could be trusted.
- A big-data ingestion pipeline in VB and C++ that sorted detector telemetry for analytics and reporting.
- Simulation before hardware: phoswich beta-gamma coincidence detectors modelled in GEANT4 on Linux, driving improvements in instrument efficiency.
- Compton imaging for defence: later, under a Defence R&D Canada contract, design and simulation of Compton-imager radiation detectors in C++ and GEANT4 — studying source localisation and energy response.
Verify it
This work was peer reviewed. Jonathan co-authored a paper on advanced detector techniques in the Journal of Radioanalytical and Nuclear Chemistry, and contributed to the BEBI ’08 symposium proceedings on biomedical electronics and informatics (pp. 91–96).
What this means for your project
It is where our habit of checking everything comes from. When we build a data pipeline, a dashboard or an AI feature for you, we trace every number back to its source — because we learned on systems where a wrong number was not an option. Start a project →