Kingston, RI-6 March, 2025-Binary Neutron Stars The observation areas are important for mounting mildly multidisciplinary astronomy. These huge stars collide, millions of light years from the earth, emit gravitational waves after light. They provide unique opportunities for the study of gravity and matter in extreme conditions, with exciting implications for atomic physics and universe.
But the methods of traditional data-analysis are very slow to fully interpret gravity waves from these multi-mentor signals, while rapid analysis is important in directing time-sensitive electromagnetic comments due to the length and complexity of the signals.
One in Study published on 5 March In Journal Nature, an interdisciplinary team of researchers offers a novel approach that uses a nervous network to analyze gravity waves from the binary neutron star merger that is much faster and more accurate than traditional methods. So, rapidly it can be done in an eye nap – the neutron is fully observed even before the merger of stars.
Maximilian dax, a Ph.D. Students at the Max Planck Institute for Intelligent Systems in Germany who led the study.
“With this process, we are able to analyze gravitational waves several thousand times faster than the best traditional method. It took about an hour, we can do in a second, ”says Michael PureerAssistant Professor at a University in Road Island Physics And one of the 10 contributions to the study.
The first address of gravity waves-NSF-funded laser interferometer gravity-wave observatory (LIGO) was observed by the first address of gravitational waves. Ligo scientific cooperation (LSC). Two years later, LSC first visited the gravitational waves produced by the merger of two binary neutron stars.
“The surprising thing is that if you have a merger of two neutrons stars, they emit radiation in electromagnetic spectrum,” said Purir, a member of Ligo Scientific Cooperation since 2013. URI is a member of LSC since 2017 and is the only member in Road Island and has very few in New England.
He said, “This allows you to learn a lot of interesting things, not just the mass -like properties of neutron stars, for neutron stars, how fast they were spinning and how far the binary was,” he said about many signs. “But also, we know that the universe is expanding and the observation of the neutron star merger provides an alternative way to measure that expansion.”
But to be able to capture the data from electromagnetic signals, observatory must work faster and draw accurate conclusions.
In his study, “real-time estimates for binary neutron star merger using machine learning,” researcher a machine learning algorithm-dingo-BNS-which saves valuable time to analyze gravity waves from the merger of binary neutron stars. (Dingo-BNS stands for deep estimates for gravitational-wave comments for binary neutron stars.) The algorithm can fully mark the systems of merging neutron stars in about a second. The fastest traditional methods take about an hour.
“We have to tell our electromagnetic partner observatories in less than a minute, less than a few seconds,” Puror said, who contributed the code to the algorithm and helped shape the paper. “This is actually a clinic. If we wait too long, the Kilonova explosion after the merger can lead to the signs already decay and they may not be able to find it. Finding both signs is the sacred grave of multi-mesnger astronomy. Kilonova, who is less bright than Supernova, confirm that binary neutron star merger is a major site where heavy elements such as gold and platinum are made in the universe. ,
The real-time method is already more accurate than the binary neutron stars colliding-the present is more accurate than the algorithm. The machine learning framework perfectly depicts the neutron star merger without adorning it in just one second that can be less accurate, which, according to the study, includes the sky position 30% more accurately.
The study brought a team of researchers from Germany, United Kingdom and America, which included Stephen R Green of Nottingham University in the team, along with Dax and Pureer; Jacob H. McCake and Bernard Sholcoff, both Max Planck Institute for Intelligent Systems; All of Alesndra Bounano, Jonathan Gair, and Nihar Gupte, all of Max Planck Institute of Gravitational Physics; Vivian Remond of Gravity Exploration Institute at Cardiff University; And Jonas Wildburger of Ellis Institute Tubingen.
“The team consisted of machine learning experts who regularly apply state -of -the -art methods from computer science to various application domains, and experts were immersed in gravitational wave astronomy and data analysis. The study reflects the effectiveness of a successful marriage between modern deep learning methods and physical domain knowledge, ”said Puror, who is also a computational scientist. Uri center for computational research In its. Pürrer is a member Usgrc Cooperation, a URI and University of Massachusetts Cooperation focused on research of gravitational waves with dozens of researchers.
Pürrer is co-organizing Workshop on learning scientific machine in gravitational wave astronomy Providence This June at Computes and Experimental Research Institute in Mathematics in June, where this line of work will be continued.
“Dingo-BNS can help inspect electromagnetic signals before and during the collision of two neutron stars one day and play an important role in preparing the field for the next generation observatories, such as such as Cosmic explorer In America and Einstein telescope To illuminate the unknown physics in the event of a substance on ultra-high density in Europe, Puror said.
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