In astroparticle physics, data analysis is a stepwise procedure, where one major step consists of a preselection of physically relevant signal examples from an overwhelming number of undesired background examples. This step generally requires estimated attributes of the examples like energy and direction. Based on these attributes, which are estimated in a first step, the separation of signal and background examples proceeds in a second step, resulting in a purified set of examples of certain signatures or particles well suited for specific physics analyses. Present telescopes do not require the data to be separated in real time. The next generation of experiments, however, requires a considerable acceleration of the procedures. The analysis is concluded in a third step, consisting in the solution of a specific inverse problem, like the reconstruction of source spectra. Simulated examples, which have to be produced in large numbers and with high precision, provide the basis for all of the aforementioned analysis steps.
The next generation of large-scale telescopes in astronomy and astroparticle physics – CTA, SKA, and IceCube-Gen2 – is going to acquire data with a new level of precision and at an unprecedented data rate of up to 1 Petabyte per day. This exceptional data rate is more than a factor of 1000 above the present state of the art, necessitating the rejection of certain examples already during data acquisition. The fact that these experiments are not going to consist of single telescopes, but of large-scale telescope arrays, poses outstanding challenges to the analysis of data obtained with these experiments and offers groundbreaking insight into the physical mechanisms that underlie the observed astrophysical sources. Resource constraints arise from the data rate, the inherent limitations in telescope communication, and the isolated telescope sites, like for example the geographic South Pole in case of IceCube.
In the third period, we intend not only to continue providing definite solutions in the construction phase of the experiments. Additionally, we intend to contribute to the solution of problems concerning communication, intelligent interaction, and the common data analysis chains of a multitude of flagship experiments. The extremely accurate application of Convolutional Neural Networks in IceCube will be extended to other use cases, and the new generation of telescope arrays will be investigated using probabilistic graphical models. The cost-effectiveness of data modeling and simulation will be improved by combining the different approaches of the second funding period.
As an especially challenging but highly rewarding task, we will extend our efforts to include the data processing and analysis chains of the planned SKA radio telescope array, which will be by far the largest and most sensitive radio telescope ever constructed. The SKA will eventually have an effective area of about a square kilometer and facilitate not only unprecedented surveys of the radio sky to complement CTA and IceCube Gen-2, but also searches for low-frequency gravitational waves. It will deliver data at a rate of 3 TB per second already in its first phase, thus offering the greatest data processing challenge in physical sciences worldwide.
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