Identification of pulsar signals in large data during data acquisition

• Hermann Heßling (DZA)

The Machine Learning-based Pipeline for Pulsar Analysis (ML-PPA) is being developed to identify eventually pulsar signals out of the huge data streams at the Square Kilometre Array Observatory (SKAO). An interdisciplinary team of astronomers and computer scientists is cooperating to realize the "software to the data" paradigm. Considerable amounts of synthetic data (digital twins) need to be generated in order to effectively train the machine learning algorithms. For this purpose, the path of pulsar signals from a source to radio telescopes is simulated. In the talk, an overview of the framework ML-PPA is given.