When two black holes merge, they emit gravitational waves that travel through spacetime at the speed of light. When these reach Earth, the signals can be detected by large detectors in the United States (LIGO), Italy (Virgo), and Japan (KAGRA). By comparing it to theoretical predictions, scientists can determine the black hole’s properties: mass, spin, orientation, position in the sky, and distance from Earth.
The Empirical Reasoning Division of the Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen and the Astrophysics and Cosmological Relativity of the Max Planck Institute for Gravitational Physics (Albert Einstein Institute/AEI) in Potsdam A team of researchers in the department has now developed a self-checking deep learning system that extracts information from gravitational wave data with great accuracy. Along the way, the system checks its own predictions about black hole coalescence parameters. This is a deep neural network with a safety net. A set of 42 gravitational waves detected from black hole mergers was successfully analyzed by the algorithm. This study was published in the journal on April 26, 2023. Physical review letter.
DINGO: Deep Neural Networks for Gravitational Wave Analysis
Researchers developed a deep neural network called DINGO (Deep Inference for Gravitational Wave Observations) to analyze the data. DINGO is trained to extract (or infer) gravitational wave source parameters from detector data. There was a press release about this in December 2021. The network learned to interpret real (observed) gravitational wave data after training with millions of simulated signals in various configurations.
trust but confirm
However, it is not possible to tell at first glance whether a deep neural network is reading the information correctly. In fact, one of the shortcomings of common deep learning systems is that incorrect results sound plausible. Therefore, MPI-IS and AEI researchers have added controls to their algorithms. Maximilian Dax, PhD student in the Empirical Reasoning Division at MPI-IS and lead author of the publication, explains: Based on these calculated parameters, gravitational waves are modeled and compared to the originally observed signal. Therefore, deep neural networks can cross-check their own results and correct them if in doubt. “
Since the algorithm controls itself, it is much more reliable than previous machine learning methods. But that’s not all. “We were surprised to find that algorithms were often able to identify unusual events—real-world data that contradicted theoretical models. We used this information to quickly ‘flag’ data for further investigation.” , and a former senior scientist at the AEI (now at the University of Nottingham).
“It can guarantee the accuracy of machine learning methods, which rarely happens in the field of deep learning. Therefore, it becomes essential for the scientific community to use algorithms to analyze gravitational wave data,” says the authors. Yes, Director of the AEI’s Astrophysics and Cosmological Relativity Division. Scientists around the world, organized by more than 1,500 researchers, are working on gravitational waves in large collaborations such as the LIGO Scientific Collaboration (LSC).
Bernhard Schölkopf, director of MPI-IS, adds: To validate the “black box” neural network approach. “
