F.A. Muller. / "May 27, 1953." / Bibliography: p. 17. / Army Signal Corps Contract DA36-039 sc-100 Project 8-102B-0 Dept. of the Army Project 3-99-10-022

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Hinge clumps are luminous knots of star formation near the base of tidal features in some interacting galaxies. We use archival Hubble Space Telescope (HST) UV/optical/IR images and Chandra X-ray maps along with Galaxy Evolution Explorer UV Spitzer IR, and ground-based optical/near-IR images to investigate the star forming properties in a sample of 12 hinge clumps in five interacting galaxies. The most extreme of these hinge clumps have star formation rates of 1-9 M yr-1, comparable to or larger than the "overlap" region of intense star formation between the two disks of the colliding galaxy system the Antennae. In the HST images, we have found remarkably large and luminous sources at the centers of these hinge clumps. These objects are much larger and more luminous than typical "super star clusters" in interacting galaxies, and are sometimes embedded in a linear ridge of fainter star clusters, consistent with star formation along a narrow caustic. These central sources have FWHM diameters of 70 pc, compared to 3 pc in "ordinary" super star clusters. Their absolute I magnitudes range from MI -12.2 to -16.5; thus, if they are individual star clusters they would lie near the top of the "super star cluster" luminosity function of star clusters. These sources may not be individual star clusters, but instead may be tightly packed groups of clusters that are blended together in the HST images. Comparison to population synthesis modeling indicates that the hinge clumps contain a range of stellar ages. This is consistent with expectations based on models of galaxy interactions, which suggest that star formation may be prolonged in these regions. In the Chandra images, we have found strong X-ray emission from several of these hinge clumps. In most cases, this emission is well-resolved with Chandra and has a thermal X-ray spectrum, thus it is likely due to hot gas associated with the star formation. The ratio of the extinction-corrected diffuse X-ray luminosity to the mechanical energy rate (the X-ray production efficiency) for the hinge clumps is similar to that in the Antennae galaxies, but higher than those for regions in the normal spiral galaxy NGC 2403. Two of the hinge clumps have point-like X-ray emission much brighter than expected for hot gas; these sources are likely "ultra-luminous X-ray sources" due to accretion disks around black holes. The most extreme of these sources, in Arp 240, has a hard X-ray spectrum and an absorbed X-ray luminosity of 2 × 1041 erg s-1; this is above the luminosity expected by single high mass X-ray binaries (HMXBs), thus it may be either a collection of HMXBs or an intermediate mass black hole (≥80 M ).

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Diese Arbeit stellt Modelle und Methoden vor, die für robuste Statistiken und ihre Anwendungen in verschiedenen Bereichen entwickelt wurden. Kapitel 2 stellt einen neuartigen Partitionierungs-Clustering-Algorithmus vor, der auf Expectiles basiert. Der Algorithmus bildet Cluster, die sich an das Endverhalten der Clusterverteilungen anpassen und sie dadurch robuster machen. Das Kapitel stellt feste Tau-Clustering- und adaptive Tau-Clustering-Schemata und ihre Anwendungen im Kryptowährungsmarkt und in der Bildsegmentierung vor. In Kapitel 3 wird ein faktorerweitertes dynamisches Modell vorgeschlagen, um das Tail-Verhalten hochdimensionaler Zeitreihen zu analysieren. Dieses Modell extrahiert latente Faktoren, die durch Extremereignisse verursacht werden, und untersucht ihre Wechselwirkung mit makroökonomischen Variablen mithilfe des VAR-Modells. Diese Methodik ermöglicht Impuls-Antwort-Analysen, Out-of-Sample-Vorhersagen und die Untersuchung von Netzwerkeffekten. Die empirische Studie stellt den signifikanten Einfluss von durch finanzielle Extremereignisse bedingten Faktoren auf makroökonomische Variablen während verschiedener Wirtschaftsperioden dar. Kapitel 4 ist eine Pilotanalyse zu Non Fungible Tokens (NFTs), insbesondere CryptoPunks. Der Autor untersucht die Clusterbildung zwischen digitalen Assets mithilfe verschiedener Visualisierungstechniken. Die durch CNN- und UMAP-Regression identifizierten Cluster werden mit Preisen und Merkmalen von CryptoPunks in Verbindung gebracht. Kapitel 5 stellt die Konstruktion eines Preisindex namens Digital Art Index (DAI) für den NFT-Kunstmarkt vor. Der Index wird mithilfe hedonischer Regression in Kombination mit robusten Schätzern für die Top-10-Liquid-NFT-Kunstsammlungen erstellt. Es schlägt innovative Verfahren vor, nämlich Huberisierung und DCS-t-Filterung, um abweichende Preisbeobachtungen zu verarbeiten und einen robusten Index zu erstellen. Darüber hinaus werden Preisdeterminanten des NFT-Marktes analysiert. / This thesis provides models and methodologies developed on robust statistics and their applications in various domains. Chapter 2 presents a novel partitioning clustering algorithm based on expectiles. The algorithm forms clusters that adapt to the tail behavior of the cluster distributions, making them more robust. The chapter introduces fixed tau-clustering and adaptive tau-clustering schemes and their applications in crypto-currency market and image segmentation. In Chapter 3 a factor augmented dynamic model is proposed to analyse tail behavior of high-dimensional time series. This model extracts latent factors driven by tail events and examines their interaction with macroeconomic variables using VAR model. This methodology enables impulse-response analysis, out-of-sample predictions, and the study of network effects. The empirical study presents significant impact of financial tail event driven factors on macroeconomic variables during different economic periods. Chapter 4 is a pilot analysis on Non Fungible Tokens (NFTs) specifically CryptoPunks. The author investigates clustering among digital assets using various visualization techniques. The clusters identified through regression CNN and UMAP are associated with prices and traits of CryptoPunks. Chapter 5 introduces the construction of a price index called the Digital Art Index (DAI) for the NFT art market. The index is created using hedonic regression combined with robust estimators on the top 10 liquid NFT art collections. It proposes innovative procedures, namely Huberization and DCS-t filtering, to handle outlying price observations and create a robust index. Furthermore, it analyzes price determinants of the NFT market.