In this thesis I present work using the MOPED algorithm to extract in a non-parametric fashion star formation histories and galaxy masses from the spectra of galaxies in the Sloan Digital Sky Survey. The recovered parameters for all galaxies are combined to give insight into the processes of star and galaxy formation on both individual galaxy and cosmic scales. The MOPED algorithm allows use of the entire spectral range, rather than concentrating on specific features, and can be used to estimate the complete star formation history without prior assumptions about its form. By combining the star formation histories of 96,545 galaxies in the redshift range 0 < z < 0:34 the cosmic star formation rate is determined from the present day to z ~ 6. The results show that the peak of star formation occurred at z ~ 0:6, and that 26% of the mass of stars in the present-day Universe was formed at z ~ 2. The average metallicity rises from Z/Z= 0:44 at high redshift to a peak of 0:8 at z ~ 1 before declining to a level around 0.25 atthe present day. Although the peak in star formation is more recent than previously thought, the sample used includes galaxies with a range of masses not accessible to traditional studies, down to a limit of L ~ 2 x 10-3L*. By cutting the sample into ranges of mass it can be seen that the redshift at which starformation activity peaks is an essentially monotonically increasing function of final stellar mass. The time of the peak in star formation ranges from z > 2 for the highest mass galaxies (MS < 1012M) to z ~ 0:2 for the lowest (MS < 1010M). A typical L* galaxy appears to have its peak at around z » 0:8. These differences in star formation with mass reconcile the redshift of the peak found in this work with the previous estimates, generally deep surveys only probe the SFR of galaxies with MS < ML*. The stellar mass calculated using the reconstructed spectra eliminates contamination from either emission lines or AGN components. Using these masses it is possible to construct the mass function for the stellar mass component of galaxies which give excellent agreement with previous works, but extend their range by more than two decades in mass to 10 7.5 < Ms/h-2M < 1012. I present both a standard Schechter fit and a fit modified to include an extra, high-mass contribution, possibly from cluster cD galaxies. The Schechter fit parameters are phi* = (7:8 +/- 0:1) £ 10-3h3Mpc-3, M* = (7.64 +/- 0.09) x 10*10h-2M and alpha = -1.159 +/- 0.008. The sample also yields an estimate for the contribution from baryons in stars to the critical density of omega b*h = (2.39+/-0.08)x10-3, in good agreement with other indicators. No evolution of the mass function in the redshift range 0:05 < z < 0:34 is apparent, indicating that almost all stars were already formed at z » 0:34 with little or no star formation activity since then and that the evolution seen in the luminosity function must be largely due to stellar fading. The star formation history can be interpreted as a measure of how gas was transformed into stars as a function of time and stellar mass: the Baryonic Conversion Tree (BCT). There is a clear correlation between early star formation activity and present-day stellar mass: the more massive galaxies have formed about 80% of their stars at z > 1, while for the less massive ones the value is only about 20%. Comparing the BCT to the dark matter merger tree indicates that star formation efficiency at z > 1 had to be high (as much as 10%) in galaxies with present-day stellar mass larger than 2 x 10*11M, if this early star formation occurred in the main progenitor. The LCDM paradigm can accommodate a large number of red objects; it is the high efficiency in the conversion from gas to stars that needs to be explained. On the other hand, in galaxies with present-day stellar mass less than 10*11M, efficient star formation seems to have been triggered at z ~ 0:2. This work shows that there is a characteristic mass (M » 10*10M) for feedback efficiency (or lack of star formation). For galaxies with masses lower than this, feedback (or star formation suppression) is very efficient while for higher masses it is not. The BCT, determined here for the first time, should be an important observable with which to confront theoretical models of galaxy formation.

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 195-199).

RR Lyrae stars are old, iron-poor, Helium-burning variable stars. RR Lyraes are extremely useful for tracing phase-space structures and metallicities within the galaxy because they are easy to identify, have consistent luminosities, and are found in large numbers in the galactic disk, bulge, and halo. Here we present a new set of spectroscopic metallicity calibrations that use the equivalent widths of the Ca II K, Hγ, and Hδ lines to calculate metallicity values. Applied to spectroscopic survey data, these calibrations will help shed light on the evolution of the Milky Way and other galaxies.

Thesis (Ph. D.)--Michigan State University. Dept. of Physics and Astronomy, 2008. / Title from PDF t.p. (viewed on July 22, 2009) Includes bibliographical references (leaves 170-174). Also issued in print.

No description available.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 139-145).

Counting stars as a means of studying the structure of the Milky Way has a long history, which has progressed significantly with the undertaking of large-area surveys. Photographic surveys have been supplanted with the advent of CCD technology by digital surveys, which provide improved data quality allowing better calibration and fainter limits to be probed reliably. The INT/WFC Photometric H Survey of the Northern Galactic Plane (IPHAS) provides broad-band r0 and i0 photometry down to 20th magnitude at Galactic latitudes jbj < 5 . In this work I make use of the opportunity that IPHAS photometry provides to create stellar number density maps of the Northern Galactic Plane. I produce preliminary maps which are used to identify and exclude poor quality data during the preparation of the second data release of the survey (DR2). By crossmatching IPHAS against the AAVSO Photometric All-Sky Survey (APASS), I derive transformations between the two photometric systems, and measure the per-IPHAS- field magnitude shifts needed to bring the two surveys in line before a global calibration can be applied. Repeating the crossmatching approach between IPHAS and the Sloan Digital Sky Survey (SDSS), I derive transformations between the two surveys and assess their agreement before and after global photometric calibration, in order to gauge the improvement achieved. The effects of incompleteness begin to affect the fainter end of any photometric survey as a consequence of confusion and sensitivity limits. I present the application of artificial source insertion on every broad-band IPHAS DR2 image in order to measure the impact of incompleteness across the entire survey. These measurements are used to construct incompleteness-corrected density maps down to magnitude limits of r0 . 19 and i0 . 18 at an angular resolution of 1 arcminute. These maps represent a unique data product which has applications in studies of Galactic structure and extinction. I perform a cluster search on the i0-band density map, which in addition to returning 71 known clusters, identifies 29 overdensities unassociated with any known clusters. I compare the stellar densities given by my maps to those in simulated versions of the Milky Way generated by models of Galactic population synthesis. I examine the Gaia Universe Model Snapshot (GUMS), a catalogue which predicts the sky as may be observed by the Gaia mission. In order to make meaningful comparisons between GUMS and IPHAS I determine transformations between the two photometric surveys. The results of the comparison are mixed. I also make use of the 2003 Besan con model of Galactic population synthesis to generate catalogues of synthetic photometry along three sightlines in the IPHAS footprint in order to test different 3D extinction prescriptions. The lowest Galactic longitudes (` 30 ) prove to be particularly challenging to emulate, suggesting 3D mapping of optical extinction in the Galactic Plane is not yet a mature art. The main problem appears to be one of underprediction of the obscuration.

No description available.

Masters of Science / Observations that push the boundaries have historically fuelled scientific breakthroughs, and these observations frequently involve phenomena that were previously unseen and unidentified. Data sets have increased in size and quality as modern technology advances at a record pace. Finding these elusive phenomena within these large data sets becomes a tougher challenge with each advancement made. Fortunately, machine learning techniques have proven to be extremely valuable in detecting outliers within data sets. Astronomaly is a framework that utilises machine learning techniques for anomaly detection in astronomy and incorporates active learning to provide target specific results. It is used here to evaluate whether machine learning techniques are suitable to detect anomalies within the optical astronomical data obtained from the Dark Energy Camera Legacy Survey. Using the machine learning algorithm isolation forest, Astronomaly is applied on subsets of the Dark Energy Camera Legacy Survey (DECaLS) data set. The pre-processing stage of Astronomaly had to be significantly extended to handle real survey data from DECaLS, with the changes made resulting in up to 10% more sources having their features extracted successfully. For the top 500 sources returned, 292 were ordinary sources, 86 artefacts and masked sources and 122 were interesting anomalous sources. A supplementary machine learning algorithm known as active learning enhances the identification probability of outliers in data sets by making it easier to identify target specific sources. The addition of active learning further increases the amount of interesting sources returned by almost 40%, with 273 ordinary sources, 56 artefacts and 171 interesting anomalous sources returned. Among the anomalies discovered are some merger events that have been successfully identified in known catalogues and several candidate merger events that have not yet been identified in the literature. The results indicate that machine learning, in combination with active learning, can be effective in detecting anomalies in actual data sets. The extensions integrated into Astronomaly pave the way for its application on future surveys like the Vera C. Rubin Observatory Legacy Survey of Space and Time.