Detecting Hidden Nuclear Sources with a New Math Tool

Topic: Physics

Scientists from the University of Michigan developed a new method to detect neutron sources. They used math tools borrowed from cosmology to improve nuclear security.

A team of researchers at the University of Michigan Engineering has found a way to directly identify neutron sources using inference tools adapted from cosmology. This breakthrough can help improve nuclear security by making it easier to intercept materials at ports or borders and guide first responders during emergency responses.

The challenge in detecting neutron sources lies in their similar energy patterns, which makes it hard to distinguish between benign industrial isotopes and fissile material. David Breitenmoser, a postdoctoral research fellow of nuclear engineering and radiological sciences at U-M, explained that this problem requires extracting reliable information from weak, noisy signals.

Currently, detectors rely on indirect signals like X-rays and gamma rays, but these can become quiet or lost as they pass through containers or shields. The new approach is more direct, quantitative, statistically rigorous, and effective in low-count measurements. It uses Bayesian modeling, a statistical method that updates confidence in hypotheses when new data becomes available.

The research team fed the messy data from a radiation detector into a model with a library of known neutron sources. The model then calculates how well the data matches each scenario, giving it a number called the Bayesian evidence. This allows the algorithm to identify the most likely source and attach a mathematical certainty to the output.

To test the math in real-world scenarios, the team placed a radiation detector in front of neutron-emitting materials and collected data from single sources or both combined. They even simulated a shielded package by placing a lead sleeve around the material. The algorithm correctly identified the source with greater than 99% confidence, even when data was sparse.

Why It Matters

This breakthrough can help prevent nuclear threats in India by improving border security and emergency response capabilities.

Key Facts

• Scientists from the University of Michigan developed a new method to detect neutron sources using inference tools adapted from cosmology.
• The new approach is more direct, quantitative, statistically rigorous, and effective in low-count measurements than current methods.
• The algorithm correctly identified neutron-emitting materials with greater than 99% confidence even when data was sparse.
• This breakthrough can help improve nuclear security by making it easier to intercept materials at ports or borders and guide first responders during emergency responses.
• The research team used Bayesian modeling, a statistical method that updates confidence in hypotheses when new data becomes available.

Key Terms

Bayesian Modeling

A statistical method that updates confidence in hypotheses when new data becomes available.

Neutron Spectra

The energy patterns of neutrons emitted by materials.

Fissile Material

Material that can undergo a chain reaction and release a large amount of energy.

Implications

This breakthrough can help prevent nuclear threats in India by improving border security and emergency response capabilities.