New paper on employing neural networks for rapid data analysis

The JGU partners have implemented a novel technique for analyzing the Cavity-RingDown data from the ULTRACHIRAL experiments. In their latest approach they are using trained autoencoder neural networks to estimate the ring-down parameters. The technique leads to levels of precision for the estimation of parameters similar to conventional, least-square-fit approaches, but at much faster rates. Check the preprint at arXiv: 2103.08663.pdf (