The quest to understand the fundamental nature of matter, energy, space, and time has led physicists to create powerful particle accelerators that collide high-energy particles at incredible speeds. These collisions produce a massive number of subatomic particles per second, making it challenging for researchers to detect and analyze them accurately.

To overcome this challenge, scientists have developed quantum sensors, specifically designed to precisely detect single particles. Researchers from the Fermi National Accelerator Laboratory (Fermilab), Caltech, NASA’s Jet Propulsion Laboratory (JPL), and other collaborating institutions have successfully tested these novel high-energy particle detection instruments at Fermilab.

The research team, led by Maria Spiropulu, used superconducting microwire single-photon detectors (SMSPDs) to detect charged particles for the first time. These sensors can precisely track particles in both space and time, achieving better spatial and time resolution simultaneously.

According to Si Xie, a scientist at Fermilab, “This is just the beginning. We have the potential to detect particles lower in mass than we could before as well as exotic particles like those that may constitute dark matter.” The quantum sensors used in this study are similar to superconducting nanowire single-photon detectors (SNSPDs), which have applications in quantum networks and astronomy experiments.

The researchers demonstrated that the SMSPD sensors were highly efficient at detecting high-energy beams of protons, electrons, and pions. This breakthrough has significant implications for future particle physics experiments, such as those planned for the Future Circular Collider or a muon collider.

“We are very excited to work on cutting-edge detector R&D like SMSPDs because they may play a vital role in capstone projects in the field,” said Fermilab scientist and Caltech alumnus Cristián Peña. The study, titled “High energy particle detection with large area superconducting microwire array,” was funded by the US Department of Energy, Fermilab, the National Agency for Research and Development (ANID) in Chile, and the Federico Santa María Technical University.

The success of this research has paved the way for further advancements in particle physics experiments, utilizing quantum sensors to optimize next-generation searches for new particles and dark matter.

The tiny parasitic worm, nematode, has long been a subject of fascination for scientists. These creatures can jump as high as 20 times their body length, which is an incredible feat considering they don’t have legs. Inspired by this remarkable ability, researchers at Georgia Tech have created a soft robot that can leap 10 feet high without any legs.

The robot’s design is based on the unique way nematodes move. They can bend their bodies into different shapes to propel themselves forward and backward. By watching high-speed videos of these creatures, the researchers were able to develop simulations of their jumping behavior. This led them to create soft robots that could replicate the leaping worms’ movement.

The key to the robot’s success lies in its ability to store energy when it kinks its body. This stored energy is then rapidly released to propel the robot forward or backward. The researchers found that by reinforcing the robot with carbon fibers, they could accelerate the jumps and make them more efficient.

This breakthrough has significant implications for robotics and engineering. With the ability to create simple elastic systems made of carbon fiber or other materials, engineers can design robots that can hop across various terrain. This technology could be used in search and rescue missions where robots need to traverse unpredictable terrain and obstacles.

Lead researcher Sunny Kumar said, “We’re not aware of any other organism at this tiny scale that can efficiently leap in both directions at the same height.” The researchers are continuing to study the unique way nematodes use their bodies to move and build robots to mimic them. This research has the potential to lead to innovative solutions for robotics and engineering.

Associate Professor Saad Bhamla’s lab collaborated on this project with researchers from the University of California, Berkeley, and the University of California, Riverside. The study was published in Science Robotics on April 23.

A groundbreaking new approach to 3D streaming has emerged, poised to revolutionize how users experience virtual reality (VR) and augmented reality (AR) environments. Researchers at NYU Tandon School of Engineering have developed a method for directly predicting visible content in immersive 3D environments, potentially reducing bandwidth requirements by up to 7-fold while maintaining visual quality.

This innovative technology addresses the fundamental challenge of streaming immersive content: the massive amount of data required to render high-quality 3D experiences. Traditional video streaming sends everything within a frame, but this new approach is more like having your eyes follow you around a room – it only processes what you’re actually looking at.

The system works by dividing 3D space into “cells” and treating each cell as a node in a graph network. It uses transformer-based graph neural networks to capture spatial relationships between neighboring cells, and recurrent neural networks to analyze how visibility patterns evolve over time. This approach reduces error accumulation and improves prediction accuracy, allowing the system to predict what will be visible for a user 2-5 seconds ahead – a significant improvement over previous systems that could only accurately predict a user’s field of view (FoV) a fraction of a second ahead.

The research team’s approach has been applied in an ongoing project to bring point cloud video to dance education, making 3D dance instruction streamable on standard devices with lower bandwidth requirements. This technology has the potential to transform the way people experience immersive content, enabling more responsive AR/VR experiences with reduced data usage and allowing developers to create more complex environments without requiring ultra-fast internet connections.

“We’re seeing a transition where AR/VR is moving from specialized applications to consumer entertainment and everyday productivity tools,” said Yong Liu, professor in the Electrical and Computer Engineering Department at NYU Tandon. “Bandwidth has been a constraint. This research helps address that limitation.”