AI Model Speeds Up Design of Giant Neutrino Detector at South Pole
Researchers have created an artificial intelligence system that simulates how neutrinos interact with ice, allowing scientists to test detector designs months faster than traditional methods. The breakthrough could reduce costly construction errors for IceCube-Gen2, a $350 million observatory hunting for cosmic particles that could unlock mysteries of black holes and the early universe.
Originaltitel: A differentiable surrogate model for the generation of radio pulses from in-ice neutrino interactions
Abstract The planned IceCube-Gen2 radio neutrino detector at the South Pole will enhance the detection of cosmic ultra-high-energy neutrinos. It is crucial to utilize the available time until construction to optimize the detector design. A fully differentiable pipeline, from signal generation to detector response, would allow for the application of gradient descent techniques to explore the parameter space of the detector. In our work, we focus on the aspect of signal generation, and propose a modularized deep learning architecture to generate radio signals from in-ice neutrino interactions conditioned on the shower energy and viewing angle. The model is capable of generating differentiable signals with amplitudes spanning multiple orders of magnitude, as well as consistently producing signals corresponding to the same underlying event for different viewing angles. The modularized approach ensures physical consistency of the samples and leads to advantageous computational properties when using the model as part of a bigger optimization pipeline.