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Ultra-cold quantum sensors cut X-ray uncertainty, improving nuclear material assessments

To monitor the amount and type of nuclear material at power plants and weapons facilities, scientists look for a special signal—the unique pattern of gamma rays emitted by specific radioactive elements. However, some of these elements also emit X-rays in the same energy range as the gamma-ray emissions, masking the signal and making nuclear stockpiles harder to assess.

Ultra-cold quantum sensors cut X-ray uncertainty, improving nuclear material assessments

Scientists at the National Institute of Standards and Technology (NIST) have achieved unprecedented accuracy in measuring X-ray emissions from plutonium, uranium, and neptunium. These elements emit X-rays within the same energy range as gamma-ray emissions, which can mask the signal and make it difficult to assess nuclear materials accurately.

The X-ray measurements were made using transition edge sensors (TESs), quantum sensors developed at NIST that act as miniature thermometers. At temperatures just a fraction of a degree above absolute zero, the superconducting film in TESs experiences a change in resistance when struck by an X-ray photon, enabling highly precise energy measurements.

By filtering out X-ray background noise, scientists can more precisely evaluate the accumulation of nuclear materials, improving assessments of nuclear stockpiles at power plants and weapons facilities. The new measurements should enable more accurate accounting of nuclear materials, reduce uncertainties in X-ray energy measurements by one-third to one-eighth, and allow for faster assessments of nuclear material composition.

Current systems require cryogenic cooling, but NIST's sensors are compatible with both in-situ monitoring at nuclear facilities and remote sample analysis in laboratories. NIST is also working on improving the accuracy of the detectors and reducing the cost and complexity of the cooling equipment.

Written by urgent.news from Phys.org's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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