In der Astroteilchenphysik ist die Datenanalyse ein schrittweises Verfahren, bei dem ein wesentlicher Schritt in der Vorauswahl physikalisch relevanter Signalbeispiele aus einer überwältigenden Anzahl von unerwünschten Hintergrundbeispielen besteht. Dieser Schritt erfordert im Allgemeinen geschätzte Attribute der Beispiele wie Energie und Richtung. Basierend auf diesen Attributen, die in einem ersten Schritt geschätzt werden, erfolgt in einem zweiten Schritt die Trennung von Signal- und Hintergrundbeispielen, was zu einem gereinigten Satz von Beispielen bestimmter Signaturen oder Partikel führt, die für spezifische physikalische Analysen gut geeignet sind. Bei bestehenden Teleskopen ist es nicht erforderlich, die Daten in Echtzeit zu trennen. Die nächste Generation von Experimenten erfordert jedoch eine erhebliche Beschleunigung der Verfahren. Die Analyse wird in einem dritten Schritt abgeschlossen, bestehend aus der Lösung eines spezifischen inversen Problems, wie der Rekonstruktion von Quellspektren. Simulierte Beispiele, die in großer Stückzahl und mit hoher Präzision produziert werden müssen, bilden die Grundlage für alle vorgenannten Analyseschritte.
Die nächste Generation von Großteleskopen der Astronomie und Astroteilchenphysik - CTA, SKA und IceCube-Gen2 - werden Daten mit einer neuen Präzision und einer beispiellosen Datenrate von bis zu 1 Petabyte pro Tag erfasst. Diese außergewöhnliche Datenrate liegt um mehr als den Faktor 1000 über dem derzeitigen Stand der Technik, so dass bestimmte Beispiele bereits bei der Datenerfassung verworfen werden müssen. Die Tatsache, dass diese Experimente nicht aus einzelnen Teleskopen, sondern aus großformatigen Teleskoparrays bestehen werden, stellt die Analyse der mit diesen Experimenten gewonnenen Daten vor große Herausforderungen und bietet einen bahnbrechenden Einblick in die physikalischen Mechanismen, die den beobachteten astrophysikalischen Quellen zugrunde liegen. Ressourcenengpässe ergeben sich aus der Datenrate, den inhärenten Einschränkungen der Teleskopkommunikation und den isolierten Teleskopstandorten, wie z.B. dem geografischen Südpol im Falle von IceCube.
Im dritten Zeitraum wollen wir nicht nur in der Konstruktionsphase der Experimente weiterhin konkrete Lösungen anbieten. Darüber hinaus wollen wir zur Lösung von Problemen der Kommunikation, der intelligenten Interaktion und der gemeinsamen Datenanalyseketten einer Vielzahl von Vorzeigeversuchen beitragen. Die extrem genaue Anwendung von Convolutional Neural Networks in IceCube wird auf weitere Anwendungsfälle ausgedehnt, und die neue Generation von Teleskop-Arrays wird mit Hilfe probabilistischer grafischer Modelle untersucht. Die Kosteneffizienz von Datenmodellierung und Simulation wird durch die Kombination der verschiedenen Ansätze der zweiten Förderperiode verbessert.
Als besonders anspruchsvolle, aber sehr lohnende Aufgabe werden wir unsere Anstrengungen auf die Datenverarbeitungs- und Analyseketten des geplanten SKA-Radioteleskop-Arrays ausdehnen, das mit Abstand das größte und empfindlichste jemals gebaute Radioteleskop sein wird. Die SKA wird schließlich eine effektive Fläche von etwa einem Quadratkilometer haben und nicht nur beispiellose Untersuchungen des Funklufthimmels als Ergänzung zu CTA und IceCube Gen-2 ermöglichen, sondern auch die Suche nach niederfrequenten Gravitationswellen. Bereits in der ersten Phase wird es Daten mit einer Rate von 3 TB pro Sekunde liefern und damit die größte Herausforderung in der Datenverarbeitung in den Naturwissenschaften weltweit darstellen.
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