Browsing by Subject "galaxy evolution"
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Item Galactic Outflows in Starburst Dwarf Galaxies Detected in Diffuse X-ray Emission(2015-12) Heilman, TarynWe present an analysis of archival Chandra X-ray observations of a sample of six nearby dwarf starburst galaxies (DDO 165, NGC 625, NGC 1569, NGC 4214, NGC 4449, NGC 5253). This work presents maps of diffuse X-ray emission with point sources removed. We find evidence of diffuse X-ray emission extended beyond the optical disc in four of six galaxies in the sample. The diffuse emission is best fit to single or two temperature thermal plasma models with temperatures of 0.14-0.8 keV. In galaxies with extended emission detected, emission extends up to 5 kpc from the nuclei of the galaxies, and all show unique morphologies. We compare X-ray data to ancillary UV and optical data from the STARBIRDS project and archival radio wavelengths. Diffuse X-ray emission generally corresponds with star forming regions emitting in the Near UV, and/or with an under-density in the HI disc. Our analysis shows that diffuse X-ray emission is strongly correlated with star formation on both short (~10 Myr) and long timescales (200-400 Myr). The shorter timescales agree with previous studies, however the agreement on long timescales suggest the picture is more complex. Our analysis combined with star formation histories conflict with previous works assuming ~10 Myr burst timescales and constant star formation rates, suggesting that longer, time-varying star formation rates are essential to truly understanding stellar feedback and galactic winds.Item Integrating Human and Machine Intelligence in Galaxy Morphology Classification Tasks(2018-01) Beck, MelanieThe large flood of data flowing from observatories presents significant challenges to astronomy and cosmology – challenges that will only be magnified by projects currently under development. Growth in both volume and velocity of astrophysics data is accelerating: whereas the Sloan Digital Sky Survey (SDSS) has produced 60 terabytes of data in the last decade, the upcoming Large Synoptic Survey Telescope (LSST) plans to register 30 terabytes per night starting in the year 2020. Additionally, the Euclid Mission will acquire imaging for ∼ 5 × 10^7 resolvable galaxies. The field of galaxy evolution faces a particularly challenging future as complete understanding often cannot be reached without analysis of detailed morphological galaxy features. Historically, morphological analysis has relied on visual classification by astronomers, accessing the human brains capacity for advanced pattern recognition. However, this accurate but inefficient method falters when confronted with many thousands (or millions) of images. In the SDSS era, efforts to automate morphological classifications of galaxies (e.g., Conselice et al., 2000; Lotz et al., 2004) are reasonably successful and can distinguish between elliptical and disk-dominated galaxies with accuracies of ∼80%. While this is statistically very useful, a key problem with these methods is that they often cannot say which 80% of their samples are accurate. Furthermore, when confronted with the more complex task of identifying key substructure within galaxies, automated classification algorithms begin to fail. The Galaxy Zoo project uses a highly innovative approach to solving the scalability problem of visual classification. Displaying images of SDSS galaxies to volunteers via a simple and engaging web interface, www.galaxyzoo.org asks people to classify images by eye. Within the first year hundreds of thousands of members of the general public had classified each of the ∼1 million SDSS galaxies an average of 40 times. Galaxy Zoo thus solved both the visual classification problem of time efficiency and improved accuracy by producing a distribution of independent classifications for each galaxy. While crowd-sourced galaxy classifications have proven their worth, challenges remain before establishing this method as a critical and standard component of the data processing pipelines for the next generation of surveys. In particular, though innovative, crowd-sourcing techniques do not have the capacity to handle the data volume and rates expected in the next generation of surveys. These algorithms will be delegated to handle the majority of the classification tasks, freeing citizen scientists to contribute their efforts on subtler and more complex assignments. This thesis presents a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides a factor of 11.4 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.Item Studying the Building Blocks of the Universe: the faint, low-mass galaxies(2018-08) Mehta, VihangFaint, low-mass galaxies are the next frontier in extending our understanding of how our universe evolved into its present-day state that we observe. As the ever-advancing technological prowess brings about the next generation of cutting-edge observational facilities, the limit down to which we can observe galaxies is constantly pushed to fainter fluxes and consequently, lower masses. With this new population of galaxies coming into focus, it also serves as a new set of subjects to test our models and theory of galaxy formation. While the current galaxy formation models have been widely successful at reproducing the general trends in observed properties of typical galaxies, they struggle to do so for galaxies in low mass halos. In simulations, the growth of the galaxies traces the growth of their parent dark matter halos too closely, which manifests as an over-prediction of low-mass galaxies compared to the observations. Feedback from star-formation and central black hole activity is necessary to decouple the evolution of the galaxies (made of baryonic material) from that of the dark matter halos. This is particularly critical for low-mass galaxies because of their shallow gravitational potential wells. The goal of this thesis is to understand the star-formation properties of faint, low-mass galaxies and to assemble statistically significant samples of these objects that can ultimately be used to perform more detailed follow-up studies and refine the galaxy formation models. Using deep UV imaging data obtained as part of the Hubble UltraViolet Ultra Deep Field (UVUDF) program, we measure the rest-UV luminosity functions for star-forming galaxies during the cosmic high-noon -- the peak of cosmic star-formation rate at 1.5Item Super-Massive Black Scaling Relations And Peculiar Ringed Galaxies(2017-06) Mutlu Pakdil, BurcinThis dissertation aims to improve the theory of galaxy formation through two independent areas of investigation: 1) super-massive black hole (BH) scaling relations and 2) formation mechanisms of peculiar rings. Several scaling relations between BH masses and numerous properties of their host galaxies have been reported in the literature, implying a co-evolution scenario of galaxies and their central BHs. The first part of this dissertation explores these scaling relations in both observations and simulations. Chapter 2 presents two important applications of the scaling relations: a determination of black hole mass function (BHMF) and a search for intermediate-mass black holes (IMBHs). I estimated a local BHMF through imaging data only by using the statistically tightest correlations. This work provided a reliable census of local BHs, especially for the low-mass regime. This chapter then focuses on my contributions to a collaborative project in the search for IMBHs, in which I provided the BH mass estimations from the spiral arm morphology. This collaboration demonstrated, for the first time, the consistency between the predictions of several popular scaling relations in the low-mass regime. Chapter 3 explores the BH−galaxy connection beyond the bulge by using the Illustris simulation. This work showed Illustris establishes very tight correlations between the BH mass and large-scale properties of the host galaxy, not only for early-type galaxies but also late-type galaxies, regardless of bar morphology. These tight relations suggest that halo properties play an important role in determining those of the galaxy and its BH. The main focus of Chapters 4 and 5 is ring formation mechanisms, in particular the origin of Hoag-type galaxies. Studying such peculiar galaxies is important to address how different kinds of interactions contribute to different galaxy morphologies. Chapter 5 presents a photometric study of PGC 1000714, a galaxy with a fair resemblance to Hoag’s Object. This work has revealed, for the first time, an elliptical galaxy with two fairly round rings. A number of formation scenarios are discussed. However, there are many questions yet to address, especially the origin of the inner ring. This dissertation is just a beginning to understanding this interesting group of galaxies.