12,167 research outputs found

    Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape

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    The Earth\u27s surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale-dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower-based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso-β, meso-γ) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid-latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface-atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller-scale turbulent and larger-scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single-point tower measurements through traditional time-domain half-hourly Reynolds decomposition. We report scale-resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface-atmospheric interactions over unstructured heterogeneities and can help inform multi-scale model-data integration of weather and climate models at a sub-grid scale

    Chiral active fluids: Odd viscosity, active turbulence, and directed flows of hydrodynamic microrotors

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    While the number of publications on rotating active matter has rapidly increased in recent years, studies on purely hydrodynamically interacting rotors on the microscale are still rare, especially from the perspective of particle based hydrodynamic simulations. The work presented here targets to fill this gap. By means of high-performance computer simulations, performed in a highly parallelised fashion on graphics processing units, the dynamics of ensembles of up to 70,000 rotating colloids immersed in an explicit mesoscopic solvent consisting out of up to 30 million fluid particles, are investigated. Some of the results presented in this thesis have been worked out in collaboration with experimentalists, such that the theoretical considerations developed in this thesis are supported by experiments, and vice versa. The studied system, modelled in order to resemble the essential physics of the experimentally realisable system, consists out of rotating magnetic colloidal particles, i.e., (micro-)rotors, rotating in sync to an externally applied magnetic field, where the rotors solely interact via hydrodynamic and steric interactions. Overall, the agreement between simulations and experiments is very good, proving that hydrodynamic interactions play a key role in this and related systems. While already an isolated rotating colloid is driven out of equilibrium, only collections of two or more rotors have experimentally shown to be able to convert the rotational energy input into translational dynamics in an orbital rotating fashion. The rotating colloids inject circular flows into the fluid, such that detailed balance is broken, and it is not a priori known whether equilibrium properties of colloids can be extended to isolated rotating colloids. A joint theoretical and experimental analysis of isolated, pairs, and small groups of hydrodynamically interacting rotors is given in chapter 2. While the translational dynamics of isolated rotors effectively resemble the dynamics of non-rotating colloids, the orbital rotation of pairs of rotors can be described with leading order hydrodynamics and a two-dimensional analogy of Faxén’s law is derived. In chapter 3, a homogeneously distributed ensemble of rotors (bulk) as a realisation of a chiral active fluid is studied and it is explicitly shown computationally and experimentally that it carries odd viscosity. The mutual orbital translation of rotors and an increase of the effective solvent viscosity with rotor density lead to a non-monotonous behaviour of the average translational velocity. Meanwhile, the rotor suspension bears a finite osmotic compressibility resulting from the long-ranged nature of hydrody- namic interactions such that rotational and odd stresses are transmitted through the solvent also at small and intermediate rotor densities. Consequently, density inhomogeneities predicted for chiral active fluids with odd viscosity can be found and allow for an explicit measurement of odd viscosity in simulations and experiments. At intermediate densities, the collective dynamics shows the emergence of multi-scale vortices and chaotic motion which is identified as active turbulence with a self-similar power-law decay in the energy spectrum, showing that the injected energy on the rotor scale is transported to larger scales, similar to the inverse energy cascade of clas- sical two-dimensional turbulence. While either odd viscosity or active turbulence have been reported in chiral active matter previously, the system studied here shows that the emergence of both simultaneously is possible resulting from the osmotic compressibility and hydrodynamic mediation of odd and active stresses. The collective dynamics of colloids rotating out of phase, i.e., where a constant torque instead of a constant angular velocity is applied, is shown to be qualitatively very similar. However, at smaller densities, local density inhomogeneities imply position dependent angular velocities of the rotors resulting from inter-rotor friction. While the friction of a quasi-2D layer of active colloids with the substrate is often not easily modifiable in experiments, the incorporation of substrate friction into the simulation models typically implies a considerable increase in computational effort. In chapter 4, a very efficient way of incorporating the friction with a substrate into a two-dimensional multiparticle collision dynamics solvent is introduced, allowing for an explicit investigation of the influences of substrate on active dynamics. For the rotor fluid, it is explicitly shown that the influence of the substrate friction results in a cutoff of the hydrodynamic interaction length, such that the maximum size of the formed vortices is controlled by the substrate friction, also resulting in a cutoff in the energy spectrum, because energy is taken out of the system at the respective length. These findings are in agreement with the experiments. Since active particles in confinement are known to organise in states of collective dynamics, ensembles of rotationally actuated colloids are studied in circular confinement and in the presence of periodic obstacle lattices in chapters 5 and 6, respectively. The results show that the chaotic active turbulent transport of rotors in suspension can be enhanced and guided resulting from edge flows generated at the boundaries, as has recently been reported for a related chiral active system. The consequent collective rotor dynamics can be regarded as a superposition of active turbulent and imposed flows, leading to on average stationary flows. In contrast to the bulk dynamics, the imposed flows inject additional energy into the system on the long length scales, and the same scaling behaviour of the energy spectrum as in bulk is only obtained if the energy injection scales, due to the mutual generation of rotor translational dynamics throughout the system and the edge flows, are well separated. The combination of edge flow and entropic layering at the boundaries leads to oscillating hydrodynamic stresses and consequently to an oscillating vorticity profile. In the presence of odd viscosity, this consequently leads to non-trivial steady-state density modulations at the boundary, resulting from a balance of osmotic pressure and odd stresses. Relevant for the efficient dispersion and mixing of inert particles on the mesoscale by means of active turbulent mixing powered by rotors, a study of the dynamics of a binary mixture consisting out of rotors and passive particles is presented in chapter 7. Because the rotors are not self-propelled, but the translational dynamics is induced by the surrounding rotors, the passive particles, which do not inject further energy into the system, are transported according to the same mechanism as the rotors. The collective dynamics thus resembles the pure rotor bulk dynamics at the respective density of only rotors. However, since no odd stresses act between the passive particles, only mutual rotor interactions lead to odd stresses leading to the accumulation of rotors in the regions of positive vorticity. This density increase is associated with a pressure increase, which balances the odd stresses acting on the rotors. However, the passive particles are only subject to the accumulation induced pressure increase such that these particles are transported into the areas of low rotor concentration, i.e., the regions of negative vorticity. Under conditions of sustained vortex flow, this results in segregation of both particle types. Since local symmetry breaking can convert injected rotational into translational energy, microswimmers can be constructed out of rotor materials when a suitable breaking of symmetry is kept in the vicinity of a rotor. One hypothetical realisation, i.e., a coupled rotor pair consisting out of two rotors of opposite angular velocity and of fixed distance, termed a birotor, are studied in chapter 8. The birotor pumps the fluid into one direction and consequently translates into the opposite direction, and creates a flow field reminiscent of a source doublet, or sliplet flow field. Fixed in space the birotor might be an interesting realisation of a microfluidic pump. The trans- lational dynamics of a birotor can be mapped onto the active Brownian particle model for single swimmers. However, due to the hydrodynamic interactions among the rotors, the birotor ensemble dynamics do not show the emergence of stable motility induced clustering. The reason for this is the flow created by birotor in small aggregates which effectively pushes further arriving birotors away from small aggregates, which eventually are all dispersed by thermal fluctuations

    A framework for experimental-data-driven assessment of Magnetized Liner Inertial Fusion stagnation image metrics

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    A variety of spherical crystal x-ray imager (SCXI) diagnostics have been developed and fielded on Magnetized Liner Inertial Fusion (MagLIF) experiments at the Sandia National Laboratories Z-facility. These different imaging modalities provide detailed insight into different physical phenomena such as mix of liner material into the hot fuel, cold liner emission, or reduce impact of liner opacity. However, several practical considerations ranging from the lack of a consistent spatial fiducial for registration to different point-spread-functions and tuning crystals or using filters to highlight specific spectral regions make it difficult to develop broadly applicable metrics to compare experiments across our stagnation image database without making significant unverified assumptions. We leverage experimental data for a model-free assessment of sensitivities to instrumentation-based features for any specified image metric. In particular, we utilize a database of historical and recent MagLIF data including Nscans=139N_{\text{scans}} = 139 image plate scans gathered across Nexp=67N_{\text{exp}} = 67 different experiments to assess the impact of a variety of features in the experimental observations arising from uncertainties in registration as well as discrepancies in signal-to-noise ratio and instrument resolution. We choose a wavelet-based image metric known as the Mallat Scattering Transform for the study and highlight how alternate metric choices could also be studied. In particular, we demonstrate a capability to understand and mitigate the impact of signal-to-noise, image registration, and resolution difference between images. This is achieved by utilizing multiple scans of the same image plate, sampling random translations and rotations, and applying instrument specific point-spread-functions found by ray tracing to high-resolution datasets, augmenting our data in an effectively model-free fashion.Comment: 17 pages, 14 figure

    Annals [...].

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    Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin

    Varastest embrüotest pärit ekstratsellulaarsed vesiikulid: potentsiaal embrüokvaliteedi markeritena ja roll embrüo-emaka suhtluses

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    Väitekirja elektrooniline versioon ei sisalda publikatsiooneViljatus on globaalne rahvatervise probleem, mis mõjutab miljoneid inimesi. Abistav reproduktiivtehnoloogia, sealhulgas in vitro viljastamine, on aidanud mitmeid viljatuid inimesi. Küll on sellel metoodikal üheks kitsaskohaks implantatsiooni ebaõnnestumine isegi morfoloogiliselt parimate embrüotega. Seetõttu toimuvad jätkuvalt uuringud tuvastamaks paremaid meetodeid, mis hindavad embrüo kvaliteeti ja ennustavad siirdamise edukust, olles peamiselt embrüokasvusöötme baasil. Rakuvälised ehk ekstratsellulaarsed vesiikulid (EV) on membraaniga ümbritsetud nanoosakesed, mida toodavad peaaegu kõik rakutüübid erinevates füsioloogilistes ja patoloogilistes konditsioonides. Nende kaudu toimub rakuvaheline suhtlus. Mitmed uuringud, eriti vähi korral, on uurinud EVde potentsiaali biomarkerina ja ravimkandursüsteemina. Antud doktoritöö uuris implantatsiooni-eelse perioodi embrüost vabanenud EVde potentsiaali embrüokvaliteedi markerina ja embrüo-emaka suhtluse vahendajana. Katsed viidi läbi kasutades veise-embrüoid ja inimrakukultuuride põhiseid eksperimentaalmudeleid. Esimene uuring tõestas, et individuaalselt kasvatatud implantatsiooni-eelse perioodi veise-embrüod eritavad EVsid kasvusöötmesse ning nende kontsentratsiooni- ja suurusprofiil sõltub embrüo kvaliteedist ja arengustaadiumist. Järgnevalt katsetati munajuharakkudel implantatsiooni-eelse perioodi embrüost pärit EVde funktsionaalsust. Katse käigus selgus, et EVd kõrge kvaliteediga embrüotest muutsid munajuharakkude geeniekspressiooni, mida aga ei teinud halva kvaliteediga embrüote EVd. Suurenenud ekspressiooniga geenide hulgas olid mitmed interferoon-τ raja interferooni stimuleerivad geenid. Interferoon-τ peetakse mäletsejaliste tiinuse tuvastusmolekuliks. See leid viitab, et munajuha tunneb ära kvaliteetse embrüo. Viimaseks uuriti embrüo EVde funktsionaalsuse spetsiifilisust. Leiti, et endomeetrium reageerib vaid embrüo päritolu EVdele. Uuringute käigus tuvastati embrüost vabanenud EVde potentsiaal ja spetsiifilisus embrüokvaliteedi biomarkerina.Infertility is a global public health problem that affects millions of people in their reproductive life. Assisted reproductive technologies (ARTs) such as in-vitro fertilization have enabled many patients to overcome this issue. However, a bottleneck in ART success is the implantation failure even after the transfer of morphologically best embryos. Hence, investigations continue to identify better or complementary methods of assessing embryo quality and predicting transfer success, mainly based on the embryo culture media. Extracellular vesicles (EVs) are membrane-bound nanoparticles released by almost all types of cells under different physiological and pathological conditions. They mediate intercellular communication. Many studies, especially related to cancer, have investigated EVs' potential as biomarkers and therapeutic drug delivery systems. This project investigated preimplantation embryo-derived extracellular vesicles as a potential embryo quality marker and a mediator of embryo-maternal communication. Experiments were performed using bovine embryos and human cell-culture based experimental models. The first study showed that individually cultured preimplantation bovine embryos release EVs to their culture media, and their concentration and size profile are dependent on the quality and development stage of embryos. Subsequently, the functionality of preimplantation embryo-derived EVs were tested in the oviduct. It was observed that EVs from good quality embryos, but not the EVs from embryos of low developmental potential quality, could alter the gene expression of the oviduct. Among the up-regulated genes, many were interferon-stimulated genes of the interferon-τ pathway. Interferon-τ is considered the pregnancy recognition molecule in ruminant pregnancy. This finding suggests that the oviduct can serve as a biosensor of embryo quality. Finally, the functional specificity of embryonic EVs were investigated. It was observed that endometrium only react to embryonic EVs but not to the non-embryonic EVs. All these studies support the potential and specificity of embryo-derived EVs as a biomarker of embryo quality.https://www.ester.ee/record=b548409

    Machine learning for managing structured and semi-structured data

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    As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen

    MSMT-CNN for Solar Active Region Detection with Multi-Spectral Analysis

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    Precisely detecting solar active regions (AR) from multi-spectral images is a challenging task yet important in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of these 3D objects, as opposed to more traditional multi-spectral imaging scenarios where all image bands observe the same scene. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR detection where different image bands (and physical locations) each have their own set of results. Different feature fusion strategies are investigated in this work, where information from different image modalities is aggregated at different semantic levels throughout the network. This allows the network to benefit from the joint analysis while preserving the band-specific information. We compare our detection method against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs (Verbeeck et al. Astron Astrophys 561:16, 2013)) and a state-of-the-art deep learning method (Faster RCNN) and show enhanced performances in detecting ARs jointly from multiple bands. We also evaluate our proposed approach on synthetic data of similar spatial configurations obtained from annotated multi-modal magnetic resonance images

    The dynamics and ISM properties of high-redshift dusty star-forming galaxies

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    In this thesis we present a range of observations of submillimetre galaxies (SMGs), a subclass of dust-obscured star-forming galaxies (DSFGs) at redshifts of z~1-5. SMGs are among the most actively star forming sources ever observed, believed to contribute significantly to the star-formation rate density (SFRD) at its peak, so-called 'cosmic noon', at z~2. Given their extreme nature, SMGs provide a strong test of galaxy formation and evolution models. Advancements in instrumentation, in particular with the Submillimetre Common-User Bolometer Area 2 (SCUBA-2) and the Atacama Large (sub-)Millimeter Array (ALMA), have driven significant progress in SMGs studies over the last decade. We have now identified samples of hundreds of SMGs in survey fields with a plethora of photometric coverage, such as the Cosmic Evolution Survey (COSMOS), the UKIDSS Ultra Deep Survey (UDS) and the Extended Chandra Deep Field Survey (ECDFS). Indeed, the main motivation of this thesis is to exploit these samples of SMGs, with a particular focus on the molecular and ionised gas properties, using state-of-the-art instrumentation such as ALMA and the Northern Extended Millimeter Array (NOEMA) for the former, and the K-band Multi-Object Spectrograph (KMOS) mounted on the Very Large Telescope for the latter. Firstly, in Chapter 2 we present CO observations of 47 SMGs, providing one of the largest and highest quality samples of its kind. With this study we demonstrate the capability of ALMA and NOEMA to undertake blind redshift scans in the 3mm waveband, and in doing so add significantly to the number of SMGs with spectroscopic redshifts, which prior to the work presented in this thesis was small. We also exploit the multi-wavelength coverage of the samples, together with the robust new spectroscopic redshifts, to model their spectral energy distributions (SEDs) with the MAGPHYS code and subsequently estimate key physical properties such as stellar masses and star-formation rates. Perhaps more importantly, this survey has allowed us to characterise the molecular gas content in the SMG population, along with its excitation properties, results from which we present in Chapter 3. We also show that the gas depletion timescale in SMGs remains constant, and given that SMGs are significant contributors to the star-formation rate density (SFRD) at z~2, the global evolution of star-formation in SMGs appears to coincide with the evolution of the molecular gas content, as opposed to any variation in star-formation efficiency. We provide a new test of the SMG population as descendants of massive local early-type galaxies, using the derived CO linewidths and baryonic masses. In Chapter 4 we present our Large Programme with KMOS which, when completed, will have observed ~400 SMGs in the COSMOS, UDS and ECDFS fields. Expanding on the work of Chapters 2 and 3 this is designed to further add to the catalog of SMGs with spectroscopic redshifts by detecting the H_alpha and/or [OIII] emission, which probes ionised gas and can also be used to estimate star-formation rates. We detail the target selection and observing strategy of this survey, before presenting early results for 43 emission line-detected sources, including the H_alpha-derived star-formation rates, the mass-metallicity relation and BPT diagram. We also compare the H_alpha, rest-frame optical/near-infrared and dust sizes where available, finding median radii of R_e = 3.6+/-0.3 kpc, R_Halpha = 4.2+/-0.4 kpc and R_dust = 1.2+/-0.3 kpc. Additionally, the sample are consistent with a median Sersic index of n=1, i.e. with an exponential disc-like light profile. The integral field spectrograph (IFS) capabilities of KMOS allow us to spatially resolve the H_alpha/[OIII] emission when it is sufficiently bright and extended, and this provides valuable diagnostics of the galaxy kinematics. Therefore, in Chapter 5 we present resolved H_alpha/[OIII] velocity and velocity dispersion maps for 36 SMGs, from which we derive rotation curves and dispersion profiles. We compare the derived kinematics of our SMGs with less active galaxies at lower redshifts, and divide the sample into 28 'ordered' sources with clear velocity gradients, and rotation curves which can be modelled as Freeman disks, and eight 'disordered' sources with much more messy velocity maps, from which little reliable kinematic information can be obtained. We measure a median rotational velocity of v_rot = 190+/-20 km/s and a median intrinsic velocity dispersion of sigma_0 = 87+/-5 km/s from the 'ordered' subset, both of which are significantly higher than the less actively star-forming galaxies to which we compare. The median ratio of rotational velocity to intrinsic velocity dispersion in the 'ordered' sample is v_rot/sigma_0 = 2.2+/-0.5, indicating that our sources are somewhat rotationally supported, and we therefore suggest that our SMG sample likely represents 'scaled-up' versions of more 'normal' star-forming galaxies, rather than merger-dominated systems
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