Spiral galaxy HI models, rotation curves and kinematic classifications Theresa B. V. Wiegert A thesis submitted to the Faculty of Graduate Studies of The University of Manitoba in partial fulfillment of the requirements of the degree of Doctor of Philosophy Department of Physics & Astronomy University of Manitoba Winnipeg, Canada 2010 Copyright (c) 2010 by Theresa B. V. Wiegert Abstract Although galaxy interactions cause dramatic changes, galaxies also continue to form stars and evolve when they are isolated. The dark matter (DM) halo may influence this evolu- tion since it generates the rotational behaviour of galactic disks which could affect local conditions in the gas. Therefore we study neutral hydrogen kinematics of non-interacting, nearby spiral galaxies, characterising their rotation curves (RC) which probe the DM halo; delineating kinematic classes of galaxies; and investigating relations between these classes and galaxy properties such as disk size and star formation rate (SFR). To generate the RCs, we use GalAPAGOS (by J. Fiege). My role was to test and help drive the development of this software, which employs a powerful genetic algorithm, con- straining 23 parameters while using the full 3D data cube as input. The RC is here simply described by a tanh-based function which adequately traces the global RC behaviour. Ex- tensive testing on artificial galaxies show that the kinematic properties of galaxies with inclination > 40 ◦, including edge-on galaxies, are found reliably. Using a hierarchical clustering algorithm on parametrised RCs from 79 galaxies culled from literature generates a preliminary scheme consisting of five classes. These are based on three parameters: maximum rotational velocity, turnover radius and outer slope of the RC. To assess the relationship between DM content and the kinematic classes, we generate mass models for 10 galaxies from the THINGS and WHISP surveys, and J. Irwin’s sample. In most cases mass models using GalAPAGOS RCs were similar to those using traditional “tilted-ring” method RCs. The kinematic classes are mainly distinguished by their rotational velocity. We con- firm correlations between increasing velocity and B-magnitude, optical disk size, and find ii earlier type galaxies among the strong rotators. SFR also increases with maximum rota- tional velocity. Given our limited subsample, we cannot discern a trend of velocity with DM halo properties such as Mhalo/Mbaryon. Using this strategy on upcoming large databases should reveal relationships between the DM halo and our kinematic classification scheme. If NGC 2841, NGC 3521 and NGC 5055 are understood to have declining RC after further investigation, this cannot be explained by the usual morphology scenarios. Acknowledgements First and foremost I would like to thank my supervisor, Dr. Jayanne English, for her ideas, help, patience, and consequently all that she has taught me during these years. I am also very grateful to Dr. Jason Fiege, the creator of GalAPAGOS, who has acted as an unofficial co-supervisor. Dr. Samar Safi-Harb provided access to her powerful computer cluster, without which it would have been nearly impossible to finish the multitude of GalAPAGOS runs. Huge thanks to Dr. Rob Swaters who taught me mass modelling, and for answering questions and giving valuable advice. Thanks to Dr. Judith Irwin for providing data for this thesis, being excellent support during JCMT observations, and for words of en- couragement. Thanks to Dr. Brian Yanny for helping with the SDSS photometry and to Dr. Chris Fluke for helping with s2plot scripts. Thanks to all members of our small but cosy astronomy society at the University of Manitoba that I have not mentioned yet: Jennifer, Heather, Harsha, Adam, Ian... And a special thanks to Maiko for awesome computer support. Huge thanks to Susan Beshta for all her hard work making it all happen! Elizabeth – thank you for reading a number of my chapters. Sofia, Eva, Alyssa, and all the wonderful friends I have not mentioned - you know who you are, and I hope you realise how much I appreciate your help throughout these years. Ett stort tack to my wonderful family who have been so near despite the great distance, and always given me support: Monica (who read the entire thesis in search of typos), Lars, Benjamin, Daniel, och Joachim. My experience in Winnipeg provided me with the greatest ‘gift’ of all: my husband David, thanks for always being there for me. I would like to acknowledge the Department of Physics & Astronomy and the Faculty of Science at the University of Manitoba for financial support. iv Thanks to the WHISP team for providing data from the WHISP survey, van der Hulst, J. M., van Albada, T. S., & Sancisi, R. 2001, ASPC series, Vol. 240. This work made use of THINGS, ‘The HI Nearby Galaxies Survey’, Walter et al. 2008, AJ, 136, 2563 This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. We acknowledge the usage of the HyperLeda database (http://leda.univ-lyon1.fr). This research has made use of the Sloan Digital Sky Survey. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating In- stitutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The SDSS Web Site is http://www.sdss.org/. This research made use of Montage, funded by the National Aeronautics and Space Administration’s Earth Science Technology Office, Computational Technnologies Project, under Cooperative Agreement Number NCC5-626 between NASA and the California Insti- tute of Technology. The code is maintained by the NASA/IPAC Infrared Science Archive. Three-dimensional visualisation was conducted with the S2PLOT programming library (D.G.Barnes, C.J.Fluke, P.D.Bourke & O.T.Parry, 2006, Publications of the Astronomical Society of Australia, 23(2), 82-93). For Pi Contents Abstract i Acknowledgements iii Table of contents ix List of Figures x List of Tables xv 1 Introduction 1 1.1 Galaxy structure and neutral hydrogen content . ...... 4 1.2 Rotationcurvesanddarkmatter . 6 1.2.1 Measuringtherotationcurve . 10 1.3 Massmodels................................... 10 1.3.1 Mass-to-luminosityratio . 11 1.3.2 Darkmatterhaloinmassmodels . 13 1.4 Galaxyclassification .............................. 15 1.5 Goals....................................... 17 1.6 Results...................................... 19 1.7 Thesisoutline.................................. 19 CONTENTS vii 2 Data: observations and processing 21 2.1 Introduction................................... 21 2.2 HIData ..................................... 23 2.2.1 Background on neutral hydrogen observations . 23 2.2.2 Selectioncriteria ............................ 28 2.2.3 THINGSdata.............................. 29 2.2.4 WHISP ................................. 31 2.2.5 HIPASS ................................. 33 2.2.6 OtherHIdata.............................. 33 2.3 Ancillarydata.................................. 34 2.3.1 Nearinfraredi-bandSDSSdata . 34 2.3.2 NGLS12CO(J=3-2) .......................... 36 3 Methods: mass models 39 3.1 Introduction................................... 39 3.2 The HI rotation curve using velocity fields and tilted ring method . 43 3.2.1 Motivation for using the Gaussian fitting method . 43 3.2.2 Deriving the velocity field . 44 3.2.3 Rotation curve derivation . 45 3.3 Gascontribution ................................ 48 3.4 Contribution of stellar matter using luminosity profiles . ......... 52 3.4.1 Derivingtheluminosityprofile. 53 3.4.2 Stellar matter rotation curves . 58 3.5 Massmodels................................... 58 3.6 Summary .................................... 60 CONTENTS viii 4 Methods and analysis: HI modelling 61 4.1 Background ................................... 61 4.1.1 Whymodelgalaxies? .......................... 61 4.1.2 Methods................................. 62 4.2 GalAPAGOS .................................. 64 4.2.1 Structureandfunction . 67 4.2.2 Theparametricmodels. 68 4.2.3 Theoutput ............................... 78 4.3 Beamconvolution................................ 78 4.4 TestingGalAPAGOS .............................. 79 4.4.1 Artificialgalaxies ............................ 79 4.5 Results...................................... 81 4.5.1 Artificial data: The GalAPAGOS “menagerie” . 81 4.5.2 GalAPAGOS testedonactualdata. 95 4.6 Conclusions ...................................101 5 Results: rotation curves and mass models 103 5.1 Introduction...................................103 5.2 Evaluation of GalAPAGOS applied to observations . 104 5.2.1 Rotation curves - GalAPAGOS vs tilted rings . 111 5.2.2 Discussion and summary of assessing GalAPAGOS models . 126 5.3 Massmodels...................................130 5.3.1 Stellar mass-to-luminosity ratios . 131 5.3.2 Mass model fits: total M/L ratios and halo parameters . 132 5.3.3 Themassmodels ............................137 5.3.4 Implications of different rotation curves . 159 CONTENTS ix 5.3.5 Halomassesandmassratios. 161 5.4 Summaryoftheresults.............................162 6 Analysis: development of a classification scheme 168 6.1 Introduction...................................168 6.2 Mathematical approach: