Radio Interferometry with Information Field Theory

Radio Interferometry with Information Field Theory

Radio Interferometry with Information Field Theory Philipp Adam Arras München 2021 Radio Interferometry with Information Field Theory Philipp Adam Arras Dissertation an der Fakultät für Physik der Ludwig–Maximilians–Universität München vorgelegt von Philipp Adam Arras aus Darmstadt München, den 14. Januar 2021 Erstgutachter: PD Torsten A. Enßlin Zweitgutachter: Prof. Jochen Weller Tag der mündlichen Prüfung: 18. März 2021 Abstract The observational study of the universe and its galaxy clusters, galaxies, stars, and planets relies on multiple pillars. Modern astronomy observes electromagnetic sig- nals and just recently also gravitational waves and neutrinos. With the help of radio astronomy, i.e. the study of a specific fraction of the electromagnetic spectrum, the cosmic microwave background, atomic and molecular emission lines, synchrotron ra- diation in hot plasmas, and many more can be measured. From these observations a variety of scientific conclusions can be drawn ranging from cosmological insights to the dynamics within galaxies or properties of exoplanets. The data reduction task is the step from the raw data to a science-ready data prod- uct and it is particularly challenging in astronomy. Because of the impossibility of independent measurements or repeating lab experiments, the ground truth, which is essential for machine learning and many other statistical approaches, is never known in astronomy. Therefore, the validity of the statistical treatment is of utmost impor- tance. In radio interferometry, the traditionally employed data reduction algorithm CLEAN is especially problematic. Weaknesses include that the resulting images of this algo- rithm are not guaranteed to be positive (which is a crucial physical condition for fluxes and brightness), it is not able to quantify uncertainties, and does not ensure consis- tency with the measured data. Additionally, CLEAN is not aware of the signal-to-noise ratio. This leads to suboptimal results regarding the image resolution. In this thesis, Bayesian imaging and calibration methods for radio interferometry, collectively referred to as resolve, are investigated. While Bayesian approaches de- liver strictly better results and solve all of the above outlined problems, they are noto- riously computationally expensive. This thesis provides the transition from Bayesian imaging algorithms being a theoretical consideration to having a specific implemen- tation that can be applied to data from modern telescopes. These improvements con- stitute a significant step towards optimal information extraction from given radio- interferometric data. By-products of this thesis enabled, among others, the three-dimensional cartogra- phy of dust in parts of the Milky Way and a new map of the Faraday galactic rotation. On top of that, it can be envisioned to transfer the developed methods to medical imag- ing in general and magneto-resonance tomography in particular. This shows that the developed methods are transferable and facilitate insights in a variety of other domains of research. v Zusammenfassung Die Beobachtung des Universums mit seinen Galaxienhaufen, Galaxien, Sternen und Planeten steht auf mehreren Säulen. Die moderne Astronomie beobachtet elektroma- gnetischen Wellen und seit Neuestem auch Gravitationswellen und Neutrinos, die die Erde aus dem Universum erreichen. Mit Hilfe von Radioastronomie, also der Beob- achtung von astronomischen Radiowellen, können der kosmische Mikrowellenhinter- grund, atomare und molekulare Übergangslinien, Synchrotron-Strahlung in heißen Plasmen und vieles mehr gemessen werden. Aus diesen Beobachtungen lassen sich eine Vielzahl von wissenschaftlichen Erkenntnissen ziehen, die von kosmologischen Fragen über die Dynamik von Galaxien zu Exoplanten reicht. Die Datenverarbeitung von astronomischen Daten ist besonders herausfordernd: Weil keine unabhängigen Messungen in Laborumgebungen durchgeführt werden kön- nen, gibt es nie Ground-Truth-Datensätze, was essenziell für Ansätze des maschinellen Lernens wäre. Deshalb ist die Richtigkeit der statistischen Methode besonders wichtig. In der Radiointerferometrie ist der traditionell eingesetzte Datenreduktionsalgorith- mus CLEAN besonders problematisch. Zu seinen Schwächen gehört, dass die resultie- renden Bilder dieses Algorithmus nicht notwendigerweise positiv sind, was eine ent- scheidende physikalische Bedingung für Flüsse oder Helligkeit ist, er gibt keine Unsi- cherheitsinformationen aus und gewährleistet keine Konsistenz mit den gemessenen Daten. Außerdem kennt CLEAN das Signal-Rausch-Verhältnis nicht, was zu subopti- malen Ergebnissen bezüglich der Bildauflösung führt. In dieser Arbeit werden bayessche Bildgebungs- und Kalibrierungsmethoden, zu- sammenfassend als resolve bezeichnet, für Radiointerferometrie vorgestellt. Bayes- sche Ansätze liefern zwar grundsätzlich bessere Ergebnisse und lösen die oben skiz- zierten Probleme alle, sind aber deutlich rechenintensiver. Diese Arbeit stellt den Über- gang von der theoretischen Betrachtung bayesscher Bildgebungsalgorithmen zu ei- ner konkreten Implementierung, die auf Daten von modernen Teleskopen angewen- det werden kann, dar. Dies ist ein wichtiger Schritt auf dem Weg zu einer optimalen Informationsextraktion aus gegebenen radio-interferometrischen Daten. Nebenprodukte dieser Arbeit ermöglichten u.a. die dreidimensionale Kartographie von Staub in Teilen der Milchstraße und eine neue Karte der galaktischen Faraday- Rotation. Darüber hinaus ist eine Übertragung der entwickelten Methoden auf die medizinische Bildgebung im Allgemeinen und Magnetresonanztomographie im Spe- ziellen denkbar. Die entwickelten Methoden sind also übertragbar und ermöglichen Erkenntnisse in einer Vielzahl von anderen Forschungsgebieten. vii Acknowledgements I would like to take the opportunity and appreciate and thank everyone who con- tributed directly or indirectly to this thesis. First and foremost, I would like to express my gratitude to Torsten Enßlin for his caring supervision, his unconditional support in the public, his outside-of-the-box thinking, his incredible speed at proofreading, in- troducing me to many nice people in the radio community, and for creating the open and friendly culture in our IFT group. I would like to thank Rüdiger Westermann for his support, our inspiring high-level conversations, and his openness for collaborating with astrophysicists. This thesis would not have been possible in this form without the massive technical, scientific, and emotional support by Martin Reinecke. I would like to thank him for all discussions related to NIFTy and ducc, sharing his thoughts and attitudes towards programming and science, and his benevolent feedback on my attempts to write code and texts. I am grateful for the great companionship by my colleagues at MPA. Especially Torsten Enßlin, Reimar Leike, Philipp Frank, Jakob Knollmüller, Sebastian Hutschen- reuter, Daniel Pumpe, and Natalia Porqueres welcomed me with open arms when I joined the IFT group. Reimar Leike advised me during the hiring process and gave important insights into the IFT group early on. I was inspired by his restless creativity and courage to dive into unknown terrain. He was a great flat mate; thank you for our cooking sessions and excessive board game nights. I cannot imagine a better of- fice mate than Philipp Frank. He spent countless hours explaining IFT and stochastic processes to me. We had numerous fruitful discussions that resulted in a great col- laboration regarding the work on NIFTy. Jakob Knollmüller developed the first mod- ern resolve prototype and provided essential help for getting me started with NIFTy and IFT. His visionary thinking related to MGVI and ‘GlobalNewton’ and innumerable hours of discussion laid the foundation for most results of this thesis. Thank you, Ivan Kostyuk and Christoph Lienhard, for the good and productive at- mosphere in our office. In general, I enormously benefited from the positive environ- ment that the ‘Enßlin lab’ provided. I am honoured having been involved advising Julian Rüstig, Fabian Kapfer, and Simon Ding during their work on the master the- sis. Additionally, I am grateful for the time I could spend in the IFT group with Ann- Kathrin Straub, Gordian Edenhofer, Jakob Roth, Johannes Harth-Kitzerow, Lukas Platz, Margret Westerkamp, Max Newrzella, Philipp Haim, Philipp Zehetner, Sara Milosevic, Sebastian Kehl, Silvan Streit, Vincent Eberle, and many more. I would like to thank the many scientists in the radio community that supported my work, invited me for talks, and shared their experience with radio interferometers. Es- pecially, Ancla Müller, Ben Hugo, Landman Bester, Oleg Smirnov, Rick Perley, Simon Perkins, and Wasim Raja helped me to gain a better understanding of radio interfer- ix Acknowledgements ometric data. Hendrik Junklewitz, my predecessor as resolve developer, shared his knowledge during the beginning of my work on radio interferometry. My collabora- tions with Landman Bester, Oleg Smirnov, Rick Perley, and the IFT group have been characterized by quick responses and positive vibes. I enjoyed our joint projects a lot. As my MPA-internal PhD committee, Simona Vegetti and Benedetta Ciardi kept an eye on the progress of my projects and helped me to reflect my work one a regular basis. Andreas Weiss and the MPA IT group provided reliable IT systems and took care of problems blazingly fast. The MPA scientific coffees may have been the catalyst for the amazing collaboration within the IFT group and the emergence of projects like NIFTy. I acknowledge

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