The subject of this thesis is the analysis and modeling of the active galactic nucleus PKS 1510-089. The aim is to present a thorough background of active galactic nuclei combined with the analysis and modeling of a specific active galactic nucleus. The results will then be  linked to previous research and theories about active galactic nuclei. The data used in the analysis were retrieved from the Fermi Gamma-ray Space Telescope. A light curve analysis that extended over 12 years provided knowledge about variability and presented four interesting flaring periods. The four periods underwent a spectral analysis, and the results showed that a log parabolic curvature could best describe all four periods. The last step before the modeling was to create spectral energy distributions for all four periods to retrieve spectral points from wavelengths other than those available from Fermi. Unfortunately, there were only sufficient data for one period. That period was later used in the modeling and resulted in a well-fitted external Compton model, which was compared, with relatively good results, with previous research.

The significant increase of astronomical data necessitates new strategies and developments to analyse a large amount of information, which no longer is efficient if done by hand. Supervised machine learning is an example of one such modern strategy. In this work, we apply the classification technique on Gaia+2MASS+WISE data to explore the usage of supervised machine learning on large astronomical archives. The idea is to create an algorithm that recognises entries with unusual infrared properties which could be interesting for follow-up observations. The programming is executed in MATLAB and the training of the algorithms in the classification learner application of MATLAB. Each catalogue; Gaia+2MASS+WISE contains ~109, 5×108 and 7×108 (The European Space Agency 2019, Skrutskie et al. 2006, R. M. Cutri IPAC/Caltech) entries respectively. The algorithms searches through a sample from these archives consisting of 765266 entries, corresponding to objects within a <500 pc range. The project resulted in a list of 57 entries with unusual infrared properties, out of which 8 targets showed none of the four common features that provide a natural physical explanation to the unconventional energy distribution. After more comprehensive studies of the aforementioned targets, we deem it necessary for further studies and observations on 2 out of the 8 targets (Nr.1 and Nr.8 in table 3) to establish their true nature. The results demonstrate the applicability of machine learning in astronomy as well as suggesting a sample of intriguing targets for further studies. / Inom astronomi samlas stora mängder data in kontinuerligt och dess tillväxt ökar snabbt för varje år. Detta medför att manuella analyser av datan blir mindre och mindre lönsama och kräver istället nya strategier och metoder där stora datamängder snabbare kan analyseras. Ett exempel på en sådan strategi är vägledd maskininlärning. I detta arbete utnyttjar vi en vägled maskininlärnings teknik kallad klassificering. Vi använder klassificerings tekniken på data från de tre stora astronomiska katalogerna Gaia+2MASS+WISE för att undersöka användningen av denna teknik på just stora astronomiska arkiv. Idén är att skapa en algorithm som identifierar objekt med okontroversiella infraröda egenskaper som kan vara intressanta för vidare observationer och analyser. Dessa ovanliga objekt är förväntade att ha en lägre emission i det optiska våglängdsområdet och en högre emission i det infraröda än vad vanligtvis är observerad för en stjärna. Programmeringen sker i MATLAB och träningsprocessen av algoritmerna i MATLABs applikation classification learner. Algoritmerna söker igenom en samling data bestående av 765266 objekt, från katalogerna Gaia+2MASS+WISE. Dessa kataloger innehåller totalt ~109, 5×108 och 7×108 (The European Space Agency 2019, Skrutskie et al. 2006, R. M. Cutri IPAC/Caltech) objekt vardera. Det begränsade dataset som algoritmerna söker igenom motsvarar objekt inom en radie av <500 pc. Många av de objekt som algoritmerna identifierade som ”ovanliga” tycks i själva verket vara nebulösa objekt. Den naturliga förklaringen för dess infraröda överskott är det omslutande stoft som ger upphov till värmestrålning i det infraröda. För att eliminera denna typ av objekt och fokusera sökningen på mer okonventionella objekt gjordes modifieringar av programmen. En av de huvudsakliga ändringarna var att introducera en tredje klass bestående av stjärnor inneslutna av stoft som vi kallar "YSO"-klassen. Ytterligare en ändring som medförde förbättrade resultat var att introducera koordninaterna i träningen samt vid den slutgiltiga klassificeringen och på så vis, identifiering av intressanta kandidater. Dessa justeringar resulterade i en minskad andelen nebulösa objekt i klassen av ”ovanliga” objekt som algoritmerna identifierade. Projektet resulterade i en lista av 57 objekt med ovanliga infraröda egenskaper. 8 av dessa objekt påvisade ingen av det fyra vanligt förekommande egenskaperna som kan ge en naturlig förklaring på dess överflöd av infraröd strålning. Dessa egenskaper är; nebulös omgivning eller påvisad stoft, variabilitet, Hα emission eller maser strålning. Efter vidare undersökning av de 8 tidigare nämnda objekt anser vi att 2 av dessa behöver vidare observationer och analys för att kunna fastslå dess sanna natur (Nr.1 och Nr.8 i tabell 3). Den infraröda strålningen är alltså inte enkelt förklarad för dessa 2 objekt. Resultaten av intressanta objekt samt övriga resultat från maskininlärningen, visar på att klassificeringstekniken inom maskininlärning är användbart på stora astronomiska datamängder.

The thesis introduces physical processes that are at work in astrophysical plasmas and reviews the current state of research related to the emission of ionizing photons, i.e. Lyman continuum (LyC). Star forming galaxies and active galactic nuclei are discussed as sources of LyC. Observations of LyC leakage at all redshifts are summarized and escape fractions are brought into a cosmological context, i.e. its implications for the reionization of the Universe, one of the major gas phase changes that was completed already after ∼1Gyr after the Big Bang at redshift z∼6.The main work focuses on observations of the local LyC leaking galaxy Tololo 1247-232. Physical properties of the interstellar medium, its porosity and neutral medium column density, could be derived using newly obtained Hubble Space Telescope (HST) data. The work is based on spectroscopy obtained with the Cosmic Origins Spectrograph (COS), as well as optical and ultraviolet multi-band imaging with the Wide Field Camera 3 (WFC3). An improved COS data reduction procedure was adopted. The recent detection of ionizing radiation emerging from Tololo 1247-232 could be confirmed. A LyC escape fraction of 6.6% was derived, in agreement with previous results. We used FUV absorption lines of Si II and Si IV as a probe of the neutral and ionized interstellar medium and find that most of the ISM gas is ionized, likely facilitating LyC escape from density bounded regions. Neutral gas covering as a function of line-of-sight velocity is derived using the apparent optical depth method. The ISM is found to be sufficiently clumpy, supporting the direct escape of LyC photons. We further report on broadband UV and optical continuum imaging as well as narrowband imaging of Lyα, Hα and Hβ. We also performed VLA 21cm imaging. The hydrogen hyperfine transition was not detected, but a deep upper limit atomic gas mass of 10^9 Mo could be derived. The upper limit gas fraction is only 20 percent. Evidence is found that the H I gas halo is relatively small compared to other Lyman Alpha emitters.

The physics pertaining to the asymptotic giant branch (AGB) phase of stellar evolution has been studied for many years. However, the mechanics behind many characteristics displayed at this stage are still not fully understood. As a member of the Long Period Variable class of stars, AGB stars are invaluable in creating three-dimensional maps of the Milky Way, the Magellanic System and other galaxies with resolved stellar populations. Variable stars can be used to determine radial distances from Earth using their periodic luminosity variations. As this type of star has unknown qualities, models of AGB populations need to be calibrated with observed data. Previous research has derived a best-fitting model using the TRILEGAL code (a TRIdimensional modeL of thE GALaxy). This model was calibrated against single-epoch luminosity functions (LFs) calculated from resolved stellar populations in the Small Magellanic Cloud (SMC). With multi-epoch data now available from the VISTA survey of the Magellanic Clouds (VMC), this best-fitting model can now be compared with the LFs as they vary with time. Firstly, statistical tests are completed to measure the extent of the LF variation between epochs and from the mean LF for both the full VMC AGB catalogue and for the oxygen-rich, carbon-rich and extreme AGB classes. Statistical tests are then performed to measure the similarity between the LFs from different epochs and the simulated LFs, again for the entire sample and the three classes. This investigation shows that, while the current best-fitting model is a good approximation of many individual epochs’ AGB LFs in the SMC to within 3σ, inclusion of multi-epoch data would make for a more robust analysis. In order to do this, it would be desirable to have more epochs with deeper and regular observations that could cover full lightcurves of some of the sources. There also seems to be a statistical difference between the inner and outer areas of the SMC, perhaps due to tidal disruptions. It would be interesting to see the results of a similar study using the LMC, which is less affected by the gravitational influence of its smaller companion. / <p>This thesis was written under the supervision of Maria-Rosa Cioni at the Leibniz Institute for Astrophysics in Potsdam. The presentation was held online due to the COVID-19 pandemic.</p>

A space camera is a promising candidate to address the non-stop rising interest for astrophysics research in the Compton regime. The MeVCube mission is intended to be launched in 2022, hosting an on-board Compton Camera. To better support the development of the instrument in this early stage, a series of feasibility studies to assess two potential launch orbits were performed. The studies were composed by a series of mission analysis simulations which permitted the characterisation of the orbital environments for the two orbital options. Several sources of background noise to the instrument were identified. The population of trapped protons and trapped electrons were simulated for the periods of Solar Minimum and Solar Maximum, as well as the levels of Galactic Cosmic Ray (GCR) flux. The performance of trade-off studies concluded that an equatorial orbit is more preferable for reducing the influence of background noise. To better estimate the environment effects at the equatorial orbit, the number of particles which can penetrate the detector shielding were simulated. The next step was to perform a series secondary studies whose aim were to simulate the induced current on the electrodes, produced by the interactions occurring within the detector. The actualisation of these simulations required the study of photon interaction with matter, the various Cadmium-Zink-Telluride (CZT) types and the how they operate, and the use of a sophisticated software to perform the appropriate simulations. COMSOL, which allows the method of FEA, was chosen as the tool to perform the simulations. The geometry of the detector voxel was primarily designed in SIEMENS NX. The geometry was inserted into COMSOL, where a number of iterations were performed to finalise the appropriate mesh size, which ensured an accurate representation of the Electric field and the Weighting potential within the detector voxel. The induced current on the electrodes was decided to be calculated via MATLAB. As a verification step it was thought useful to firstly plot the weighting potential of the three electrodes under test; the chosen anode pixel, the steering grid and the cathode. The process revealed a series of numerical errors, most likely introduced by the type of mesh chosen or by the data manipulation process via MATLAB. Significant reduction of the numerical errors would lead to more accurate values for the induced current. Unfortunately, due to time constraints this was a task that was not completed. Solving this problem would be optimal for future studies with MATLAB, as the induced current on the electrodes can be correctly calculated based on charge transport within the detector bulk. / MeVCube, DESY

As of June 2020, the first all-sky X-ray survey with the eROSITA instrument aboard the spacecraft Spektr-RG has been completed. A high percentage of the 1.1 million objects included in the survey are expected to be active galactic nuclei (AGN). Such an extensive catalogue of X-ray sources offers a unique opportunity for large scale observations of distinct classes of X-ray emitters. This report explores methods of refining the catalogue to include only candidates for lensed AGN. Of the differing types of AGN known, quasi-stellar objects, or QSOs, are some of the most luminous, meaning they are well-suited for observation over large distances. This is particularly befitting for observation of gravitationally lensed objects as, for lensing effects to take place, large distances are required over which more faint objects would not be able to be viewed. An indication of strong gravitational lensing is several images of the same object seen in close proximity on the sky. In order to reduce the data to more likely candidates, counterparts within a given radius are found in the second data release from Gaia; a survey in the optical with higher resolution than eROSITA. An algorithm is produced which removes most likely stellar Gaia sources using their X-ray to optical flux ratios and astrometry parameters. The Gaia sources which have no neighbours within another given radius are then also removed, leaving a catalogue of potential multiply lensed QSOs. This automated script was then applied to an eROSITA catalogue and the results compared with known lenses. The remaining sources were also checked visually using Pan-STARRS optical survey data. The results seem to be promising, although a great deal further refinement is needed through visual inspection to find the most promising candidates for lensed QSOs. / <p>Written under the joint supervision of Georg Lamer at the Leibniz Institute for Astrophysics in Potsdam. The presentation was held online at the Institute due to the COVID-19 pandemic.</p>

Blazars are a type of Active Galaxy that emit strong astrophysical jets. The association of a HE gamma-ray flare from the blazar TXS 0506+056 to the IceCube-170922A neutrino event in 2017, opened the possibility to a link between these two events. In this thesis, we will look at the HE gamma-ray behavior of TXS 0506+056 using data obtained from the Fermi-LAT by taking into account the other set of neutrino events associated with this source from 2014-2015. We will investigate whether both neutrino events present with comparable HE gamma-ray behavior by analyzing the lightcurves and the spectra for a quiet state, the 2014-2015 period, and the flare centered around the neutrino event from 2017. The results of the analysis performed in this thesis show no strong indication of a change in the gamma-ray behaviour in these potential neutrino detections.

The Milky Way's halo is an approximately spherical distribution of stars surrounding the Galaxy that carries the history of the Milky Way. The outer halo is a Galactic region with long dynamical timescales largely built up by accreted material. Probing its stellar constituents has been historically difficult due to the distances of outer halo stars, making them appear faint. To characterise the distant halo and unravel the history of our galaxy, we thus need to use stars that are intrinsically bright, i.e. giant stars. To draw useful conclusions about the distant halo, these target giants should have metallicity and kinematics information. Therefore a catalogue of distant halo giants with Pristine survey metallicities, Gaia mission data and distances has been created in this work. The cuts used to create this catalogue are made to remove as many dwarf stars as possible and have been tested on a training sample containing spectroscopic metallicities and surface gravities as well as Gaia mission data. Defining giants as being all stars with log(g) &lt; 3.5 dex, we can calculate the purity and completeness of the sample after the cuts have been applied to test which cuts optimise the catalogue. The methods used to cut away the dwarfs are to first plot all stars with positive Gaia parallaxes and fractional parallax uncertainties smaller than 50% in a colour-absolute magnitude diagram and remove all stars from the sample that in this plot populate the main sequence. We then colour-code the colour-apparent magnitude diagram by purity and completeness after this parallax cut has been performed, and select a region in this diagram in which both purity and completeness are maximised, with the final region being (GBP,0 - GRP,0) &gt; 0.8 and G0 &lt; 17.6. The distances to the stars in this region are then computed by comparing their apparent magnitudes to the absolute ones of isochrones. These cuts are then applied to a sample of 6,884,547 stars with Pristine survey and Gaia mission data. The final catalogue is kinematically unbiased and contains 345,303 halo giants. It contains 78% giants and only 4% of giants are erroneously deselected.  With the final sample we are able to probe as deep as 103 kpc into the halo and have created preliminary metallicity distribution functions of different regions of the halo. This sample will be used to further investigate the distant halo metallicity structure and its substructure that was created through merger events.

This project aims to investigate neural network systems as an effective tool for the in-space captured dust impact signal detection. Cosmic dust is the nanometre to micrometre fine-sized particles that exist in the interplanetary region. They originate from comets, asteroids, the planets and their moons and rings, or even the interstellar region. Some are visible to the human eye as, for instance, zodiacal light. However, most dust grains are sparsely spread in space and can be captured only by in-situ measurements. One method to capture such a small grain in space utilizes the whole spacecraft’s surface as a detector and uses the onboard electric field measurement to identify their impact signals. Those signals are highly non-linear and often identified manually. A neural network system is a possible solution to improve dust detection for a massive dataset.The European Space Agency’s (ESA) solar physics mission, Solar Orbiter, has electric field measurement (PWI) onboard and can detect the micrometeorite impact signals near the sun. We used two types of PWI datasets to investigate the use of neural network systems in interplanetary dust detection.We first used a pre-trained neural network to the High-Frequency (HF) Time Domain Sampler (TDS) data to adapt further to the new dataset. We were able to obtain good detection classifications as the previous work except for the data with high time resolution, which has not been used for the pre-training before. Therefore, we implemented extra preprocessing to enable classification of data with high time resolution.We trained and tested another neural network on another type of PWI dataset, that is, the Low-Frequency (LF) continuous data. This data type is different from the TDS data type in that it does not come in packets but as a continuous data stream covering an entire day and has a lower sampling frequency. Which required different preprocessing-procedures.Based on the two types of neural network analysis we use above; we have finally been able to investigate the characteristics of dust distribution in the interplanetary region. Using the statistical analysis obtained by the SolO/PWI between April of 2020 to Mars of 2023, among others, the following characteristics have been found: The neural network analysed dust impact rates show a similar trend as onboard processed dust impact rates. Dust impact amplitude was found to be correlated to distance from the sun, spacecraft velocity, and spacecraft radial velocity. The impact rate increases as the spacecraft travels sunward. Much of the dust appears to have speeds lower than the spacecraft. Overall, from this study, we concluded that the HF neural network is better in dust signal detection, but the LF network can be improved. Shortcomings and possible improvements are presented in the conclusions.

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