Hosts and environments of low luminosity active galaxies in the local universe: the care and feeding of weak AGN

The observed relationship between the mass of a galaxy’s supermassive black hole and the galaxy’s bulge mass suggests a relationship between the growth of the galaxy and the growth of its central black hole. When these black holes grow, they release phenomenal amounts of energy into their surroundings, possibly disrupting further growth of the galaxy. The feeding (inflowing matter) and feedback (outflowing energy) of a galaxy’s central black hole may be intimately related to the properties of the host’s environment, on scales many orders of magnitude beyond the black hole’s gravitational influence. While feeding, a massive black hole reveals itself as an Active Galactic Nucleus (AGN), but only a few percent of all galaxies show evidence of an AGN. This thesis focuses on this question: What distinguishes galaxies that are currently hosting actively accreting black holes from those that are not?We use the vast data set provided by the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) to study the environments of a well defined sample of AGN hosts. To reduce contamination by galaxies that do not harbor actively accreting black holes, we define a clear, unambiguous sample of local AGN. Using this sample, we search for AGN in merging galaxies and measure the 2-point cross-correlation function of AGN and all galaxies to estimate the envrionments of AGN hosts compared to non-AGN hosts. We also describe trends in different subsamples of AGN, including luminosity and classification sub-type. Finally, we show how these techniques may be applied to future data sets such as forthcoming SDSS III data and X-ray data from the eROSITA satellite.Ph.D., Physics -- Drexel University, 201

Inferring the parallax of Westerlund 1 from Gaia DR2

Westerlund 1 (Wd1) is potentially the largest star cluster in the Galaxy. That designation critically depends upon the distance to the cluster, yet the cluster is highly obscured, making luminosity-based distance estimates difficult. Using {\it Gaia} Data Release 2 (DR2) parallaxes and Bayesian inference, we infer a parallax of $0.35^{+0.07}_{-0.06}$ mas corresponding to a distance of $2.6^{+0.6}_{-0.4}$ kpc. To leverage the combined statistics of all stars in the direction of Wd1, we derive the Bayesian model for a cluster of stars hidden among Galactic field stars; this model includes the parallax zero-point. Previous estimates for the distance to Wd1 ranged from 1.0 to 5.5 kpc, although values around 5 kpc have usually been adopted. The {\it Gaia} DR2 parallaxes reduce the uncertainty from a factor of 3 to 18\% and rules out the most often quoted value of 5 kpc with 99\% confidence. This new distance allows for more accurate mass and age determinations for the stars in Wd1. For example, the previously inferred initial mass at the main-sequence turn-off was around 40 M$_{\odot}$; the new {\it Gaia} DR2 distance shifts this down to about 22 M$_{\odot}$. This has important implications for our understanding of the late stages of stellar evolution, including the initial mass of the magnetar and the LBV in Wd1. Similarly, the new distance suggests that the total cluster mass is about four times lower than previously calculated.Comment: 14 pages, 10 figure

Source Matching in the SDSS and RASS: Which Galaxies are Really X-ray Sources?

The current view of galaxy formation holds that all massive galaxies harbor a massive black hole at their center, but that these black holes are not always in an actively accreting phase. X-ray emission is often used to identify accreting sources, but for galaxies that are not harboring quasars (low-luminosity active galaxies), the X-ray flux may be weak, or obscured by dust. To aid in the understanding of weakly accreting black holes in the local universe, a large sample of galaxies with X-ray detections is needed. We cross-match the ROSAT All Sky Survey (RASS) with galaxies from the Sloan Digital Sky Survey Data Release 4 (SDSS DR4) to create such a sample. Because of the high SDSS source density and large RASS positional errors, the cross-matched catalog is highly contaminated by random associations. We investigate the overlap of these surveys and provide a statistical test of the validity of RASS-SDSS galaxy cross-matches. SDSS quasars provide a test of our cross-match validation scheme, as they have a very high fraction of true RASS matches. We find that the number of true matches between the SDSS main galaxy sample and the RASS is highly dependent on the optical spectral classification of the galaxy; essentially no star-forming galaxies are detected, while more than 0.6% of narrow-line Seyferts are detected in the RASS. Also, galaxies with ambiguous optical classification have a surprisingly high RASS detection fraction. This allows us to further constrain the SEDs of low-luminosity active galaxies. Our technique is quite general, and can be applied to any cross-matching between surveys with well-understood positional errors.Comment: 10 pages, 10 figures, submitted to The Astronomical Journal on 19 June 200

Investigating interoperability of the LSST Data Management software stack with Astropy

The Large Synoptic Survey Telescope (LSST) will be an 8.4m optical survey telescope sited in Chile and capable of imaging the entire sky twice a week. The data rate of approximately 15TB per night and the requirements to both issue alerts on transient sources within 60 seconds of observing and create annual data releases means that automated data management systems and data processing pipelines are a key deliverable of the LSST construction project. The LSST data management software has been in development since 2004 and is based on a C++ core with a Python control layer. The software consists of nearly a quarter of a million lines of code covering the system from fundamental WCS and table libraries to pipeline environments and distributed process execution. The Astropy project began in 2011 as an attempt to bring together disparate open source Python projects and build a core standard infrastructure that can be used and built upon by the astronomy community. This project has been phenomenally successful in the years since it has begun and has grown to be the de facto standard for Python software in astronomy. Astropy brings with it considerable expectations from the community on how astronomy Python software should be developed and it is clear that by the time LSST is fully operational in the 2020s many of the prospective users of the LSST software stack will expect it to be fully interoperable with Astropy. In this paper we describe the overlap between the LSST science pipeline software and Astropy software and investigate areas where the LSST software provides new functionality. We also discuss the possibilities of re-engineering the LSST science pipeline software to build upon Astropy, including the option of contributing affliated packages

Low metallicities and old ages for three ultra-diffuse galaxies in the Coma cluster

A.W. acknowledges support of a Leverhulme Trust Early Career Fellowship.A large population of ultra-diffuse galaxies (UDGs) was recently discovered in the Coma cluster. Here we present optical spectra of three such UDGs, DF 7, DF 44, and DF 17, which have central surface brightnesses of μ g ≈ 24.4–25.1 mag arcsec−2. The spectra were acquired as part of an ancillary program within the SDSS-IV MaNGA Survey. We stacked 19 fibers in the central regions from larger integral field units (IFUs) per source. With over 13.5 hr of on-source integration, we achieved a mean signal-to-noise ratio in the optical of 9.5 Å−1, 7.9 Å−1, and 5.0 Å−1, respectively, for DF 7, DF 44, and DF 17. Stellar population models applied to these spectra enable measurements of recession velocities, ages, and metallicities. The recession velocities of DF 7, DF 44, and DF 17 are ${6599}_{-25}^{+40}$ km s−1, ${6402}_{-39}^{+41}$ km s−1, and ${8315}_{-43}^{+43}$ km s−1, spectroscopically confirming that all of them reside in the Coma cluster. The stellar populations of these three galaxies are old and metal-poor, with ages of ${7.9}_{-2.5}^{+3.6}$ Gyr, ${8.9}_{-3.3}^{+4.3}$ Gyr, and ${9.1}_{-5.5}^{+3.9}$ Gyr, and iron abundances of [Fe/H] $-{1.0}_{-0.4}^{+0.3}$, $-{1.3}_{-0.4}^{+0.4}$, and $-{0.8}_{-0.5}^{+0.5}$, respectively. Their stellar masses are (3–6) × 108M⊙. The UDGs in our sample are as old or older than galaxies at similar stellar mass or velocity dispersion (only DF 44 has an independently measured dispersion). They all follow the well-established stellar mass–stellar metallicity relation, while DF 44 lies below the velocity dispersion-metallicity relation. These results, combined with the fact that UDGs are unusually large for their stellar masses, suggest that stellar mass plays a more important role in setting stellar population properties for these galaxies than either size or surface brightness.Publisher PDFPeer reviewe

SDSS-III Baryon Oscillation Spectroscopic Survey data release 12 : galaxy target selection and large-scale structure catalogues

The Baryon Oscillation Spectroscopic Survey (BOSS), part of the Sloan Digital Sky Survey (SDSS) III project, has provided the largest survey of galaxy redshifts available to date, in terms of both the number of galaxy redshifts measured by a single survey, and the effective cosmological volume covered. Key to analysing the clustering of these data to provide cosmological measurements is understanding the detailed properties of this sample. Potential issues include variations in the target catalogue caused by changes either in the targeting algorithm or properties of the data used, the pattern of spectroscopic observations, the spatial distribution of targets for which redshifts were not obtained, and variations in the target sky density due to observational systematics. We document here the target selection algorithms used to create the galaxy samples that comprise BOSS. We also present the algorithms used to create large-scale structure catalogues for the final Data Release (DR12) samples and the associated random catalogues that quantify the survey mask. The algorithms are an evolution of those used by the BOSS team to construct catalogues from earlier data, and have been designed to accurately quantify the galaxy sample. The code used, designated mksample, is released with this paper.Publisher PDFPeer reviewe