Quantum optics tools now probe X-ray material properties

Researchers are applying a technique originally used to measure the radii of stars to the study of materials at the nanoscale. Shaul Katznelson and colleagues connected Hanbury Brown and Twiss interferometry, first demonstrated in 1956 to measure stellar radii through photon-correlation detection, to scintillation, the emission of light from materials excited by X-rays. The team quantified scintillator properties using photon correlations, measuring both emission time and the number of optical photons released per X-ray photon; this approach is particularly valuable for nano- and microscale scintillators, which pose challenges for conventional characterization.

X-ray-Driven Hanbury Brown and Twiss Spectroscopy Applied to Scintillation

Hanbury Brown and Twiss interferometry, first utilized in 1956 to determine stellar radii through photon-correlation detection, now offers a novel approach to characterizing materials at the nanoscale. The researchers successfully extracted scintillation properties from quantum-dot superlattices measuring only hundreds of nanometers, observing a result of greater than 50, indicating a high degree of photon correlation within these tiny structures.

By benchmarking their approach on a diverse range of scintillators, including rare-earth-doped and undoped oxide single crystals and perovskite nanocrystals, the team demonstrated the technique’s sensitivity to both temperature and X-ray flux variations. This detailed analysis allows for a deeper understanding of how these materials respond to different excitation conditions.

The implications of this work extend beyond improving scintillator characterization, signaling a broader integration of quantum optics tools into materials science. The researchers state that their research supports the broader use of methods from quantum optics for studying materials with complex optical properties in extreme regions of the electromagnetic spectrum, suggesting a future where techniques traditionally reserved for the study of light itself are routinely applied to the analysis of matter.

The study’s findings build on previous work exploring the relationship between photon statistics and luminescence, referencing research into photon bunching in cathodoluminescence from 2015 and investigations into Poissonian distributions in physics dating back to 2021. The team’s method offers a pathway to probe hidden material properties and promises to unlock new insights into the behavior of materials at the quantum level. Their findings were presented at CLEO 2026, focusing on high-order coincidence in X-ray/electron-luminescence.

Photon Correlation Function Quantifies Scintillator Emission

The application of photon correlation analysis, specifically the function, represents a shift in how scintillator materials are characterized, moving beyond traditional methods focused on total light output to detailed analysis of emission dynamics. This technique reveals the degree of photon bunching, or anti-bunching, within the emitted light, offering insights into the fundamental processes governing scintillation at a level previously inaccessible. This refined approach is particularly valuable when investigating nano- and microscale scintillators, materials increasingly utilized in advanced imaging and detection technologies where conventional characterization techniques struggle to provide accurate data.

The researchers observed strong photon bunching, with values greater than 50, indicating a high degree of correlation between emitted photons. By probing the statistical properties of emitted photons, researchers can uncover hidden material characteristics and gain a deeper understanding of light-matter interactions at the nanoscale.

Characterization of Scintillator Properties Across Diverse Materials

Shaul Katznelson and colleagues are applying a technique originally developed for astronomical measurements to the detailed characterization of scintillators, materials that emit light when struck by ionizing radiation. The team’s investigation of perovskite nanocrystals aligns with growing interest in these materials as potential high-resolution imaging and timing components, as evidenced by recent publications exploring their scintillation properties. The team will present their findings on probing hidden material properties at CLEO 2026, showcasing the technique’s potential for wider adoption within the scientific community.

Nanoscale Scintillator Analysis Reveals Strong Photon Bunching

This adaptation allows for a detailed analysis of how materials respond to high-energy particle bombardment, offering insights previously inaccessible with conventional methods. This approach differs from traditional methods by focusing on the statistical properties of emitted photons, providing a more nuanced understanding of the scintillation process. The ability to discern these subtle variations is crucial for optimizing materials used in applications ranging from medical imaging to security screening.

Conventional characterization methods struggle with these small dimensions, but the X-ray-driven Hanbury Brown and Twiss spectroscopy overcomes these limitations. This level of detail allows for a deeper understanding of energy transfer mechanisms within these nanoscale structures. By bridging the gap between astronomical observation and materials science, this innovative approach opens new avenues for exploring and optimizing materials at the smallest scales, promising advancements in diverse technological fields.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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