{"id":229726,"date":"2026-02-19T14:14:14","date_gmt":"2026-02-20T00:14:14","guid":{"rendered":"https:\/\/www.hawaii.edu\/news\/?p=229726"},"modified":"2026-02-19T14:16:16","modified_gmt":"2026-02-20T00:16:16","slug":"new-algorithm-aip-advances","status":"publish","type":"post","link":"https:\/\/www.hawaii.edu\/news\/2026\/02\/19\/new-algorithm-aip-advances\/","title":{"rendered":"New algorithm could improve imaging, AI<\/abbr>, particle research and more"},"content":{"rendered":"Reading time: <\/span> 2<\/span> minutes<\/span><\/span>
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The algorithm development was led by physics undergraduate Jeffrey G. Yepez.<\/figcaption><\/figure>\n

A University of Hawaiʻi<\/span> at Mānoa<\/span> student-led team has developed a new algorithm to help scientists determine direction in complex two-dimensional (2D<\/abbr>) data, with potential applications ranging from particle physics to machine learning. The research was published February 6 in AIP<\/abbr> Advances<\/em> as a featured article<\/a>.<\/p>\n

The algorithm development, led by physics<\/a> undergraduate Jeffrey G. Yepez, helps scientists figure out where tiny, nearly invisible particles called neutrinos are coming from. These particles can reveal information about nuclear reactors, the sun, and faraway cosmic events. The method is based on a clever math discovery: the team found a formula that lets them match patterns in data and accurately pinpoint the direction of the source.<\/p>\n

The students were guided by 糖心视频<\/abbr> Mānoa<\/span> Professor John G. Learned and received additional mentoring from 糖心视频<\/abbr> alumnus and Lawrence Livermore National Laboratory staff scientist Viacheslav Li, thanks to funding from the Consortium for Monitoring, Technology and Verification. The project began with simulated neutrino data to locate nuclear reactors, and further studies are underway.<\/p>\n

“What excites us most is that this approach gives researchers a clearer mathematical foundation for extracting direction from noisy, real-world data,” Yepez said. “It is a tool that scales with technological improvements in detectors, computing power and data volume, making it valuable far beyond the initial physics application.”<\/p>\n

The algorithm uses a mathematical tool called the Frobenius norm to measure differences between grids of numbers, effectively acting as a “distance formula” for large data tables. By rotating a reference dataset and comparing it to measured data, the algorithm identifies the rotation that produces the smallest difference, revealing the most likely direction of the signal. Simulations show the method works especially well with high-resolution data and large datasets.<\/p>\n

While motivated by neutrino detection, the approach could be applied across multiple fields. Potential uses include astronomy, medical imaging, weather mapping, and any system that relies on pattern recognition, offering a versatile new tool for scientists and engineers.<\/p>\n

Other 糖心视频<\/abbr> Mānoa<\/span> authors on the paper include Jackson D. Seligman, Max A. A. Dornfest and Brian C. Crow. The Department of Physics and Astronomy is part of 糖心视频<\/abbr> Mānoa<\/span>\u2019s College of Natural Sciences<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

The algorithm development helps scientists figure out where tiny, nearly invisible particles called neutrinos are coming from.<\/p>\n","protected":false},"author":16,"featured_media":229727,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[30],"tags":[308,1467,1363,568,124,158,9,1450],"class_list":["post-229726","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research","tag-college-of-natural-sciences","tag-manoa-excellence-in-research","tag-manoa-research","tag-natural-science","tag-physics","tag-publication","tag-uh-manoa","tag-undergraduate","entry","has-media"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t