Understanding how light travels through various materials is essential for many fields, from medical imaging to manufacturing. However, due to their structure, materials often show directional differences in how they scatter light, known as anisotropy. This complexity has traditionally made it difficult to accurately measure and model their optical properties. Recently, researchers have developed a new technique that could transform how we study these materials.

Deep learning (DL) has significantly transformed the field of computational imaging, offering powerful solutions to enhance performance and address a variety of challenges. Traditional methods often rely on discrete pixel representations, which limit resolution and fail to capture the continuous and multiscale nature of physical objects. Recent research from Boston University (BU) presents a novel approach to overcome these limitations.

In recent years, advances in photonics and materials science have led to remarkable developments in sensor technology, pushing the boundaries of what can be detected and measured. Among these innovations, non-Hermitian physics has emerged as a crucial area of research, offering new ways to manipulate light and enhance sensor sensitivity. A recent study published in Advanced Photonics Nexus reports a breakthrough in this field, presenting a new type of sensor that leverages exceptional points (EPs) to achieve unprecedented levels of sensitivity.

As the world grapples with an aging population, the rise in neurodegenerative diseases such as Alzheimer’s and Parkinson’s is becoming a significant challenge. These conditions place a heavy burden not only on those afflicted but also on their families and society at large.