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NASA’s SPHEREx space observatory, launched on March 11, is mapping the entire sky to chart the positions of hundreds of millions of galaxies in 3D. The spacecraft began regular science operations on May 1, taking about 3,600 images per day for two years to provide new insights into the origins of the universe, galaxies, and the ingredients for life in the Milky Way. The mission is part of NASA’s suite of space-based astrophysics survey missions leading up to the launch of NASA’s Nancy Grace Roman Space Telescope, and will play a key role in answering the big questions about the universe.

Mission goals

According to NASA’s official website, SPHEREx, an observatory in Earth orbit, will complete over 11,000 orbits over 25 months, circling Earth 14½ times a day. It takes images along one circular strip of the sky, and as the planet moves around the Sun, its field of view shifts. SPHEREx takes about 600 exposures per day, each capturing different wavelengths of light. When one exposure is completed, the observatory shifts position, and the mirrors and detectors don’t move.

Instead of using thrusters, SPHEREx relies on a system of reaction wheels to control its orientation. The observatory will look out into space in every direction after six months. The observatory’s field of view shifts as the planet moves around the Sun.

Spectroscopic insight

SPHEREx is the first mission to map the entire sky in 102 colors of infrared light, using spectroscopy to separate light into wavelengths. This allows scientists to determine distances to galaxies, turning 2D maps into 3D ones and tracing the glow of all galaxies through time. Spectroscopy also reveals the chemical makeup of cosmic objects, helping identify water and life-related compounds.

SPHEREx will make over 9 million observations of interstellar clouds in the Milky Way, mapping their distribution and chemistry. These insights will help scientists understand how different cosmic environments influenced the development of life’s essential ingredients on Earth.

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AI Model Learns to Predict Human Gait for Smarter, Pre-Trained Exoskeleton Control

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Scientists at Georgia Tech have created an AI technique that pre-trains exoskeleton controllers using existing human motion datasets, removing the need for lengthy lab-based retraining. The system predicts joint behavior and assistance needs, enabling controllers that work as well as hand-tuned versions. This advance accelerates prototype development and could improve…

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Scientists Build One of the Most Detailed Digital Simulations of the Mouse Cortex Using Japan’s Fugaku Supercomputer

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Researchers from the Allen Institute and Japan’s University of Electro-Communications have built one of the most detailed mouse cortex simulations ever created. Using Japan’s Fugaku supercomputer, the team modeled around 10 million neurons and 26 billion synapses, recreating realistic structure and activity. The virtual cortex offers a new platform for studying br…

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UC San Diego Engineers Create Wearable Patch That Controls Robots Even in Chaotic Motion

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UC San Diego engineers have developed a soft, AI-enabled wearable patch that can interpret gestures with high accuracy even during vigorous or chaotic movement. The armband uses stretchable sensors, a custom deep-learning model, and on-chip processing to clean motion signals in real time. This breakthrough could enable intuitive robot control for rehabilitation, indus…

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