Collaborative Research: Massive Surveys for Pulsars and Radio Transients Using the Arecibo L-band Feed Array

点击翻译按钮获取中文摘要

AST 0507797Jay HolbergUniversity of ArizonaThe Local Degenerate Star Population: Calibrating a Vast New DomainABSTRACTA program to comprehensively study the local white dwarf population will be undertakento address fundamental questions concerning the Galactic origin (thin disk, thick disk or halo) ofboth individual white dwarfs as well as the spectroscopic subpopulations of these stars. Thisstudy will also provide important details on the thermal and chemical evolution of white dwarfsand link the local white dwarf population with larger more distant samples such as the white dwarfpopulation in the Sloan Digital Sky Survey. The population for this study is the 2000 to 2500 whitedwarfs listed in the Villanova Catalog of Spectroscopically Identified White Dwarf. These goals areachieved through the use of newly available photometric, spectroscopic and proper motion datato augment existing data on this population of predominantly local white dwarfs. A fundamentalaspect of this proposal is the maintenance and upgrading of the existing Villanova Catalog ofSpectroscopically Identified White Dwarfs and the (Arizona) White Dwarf Database, in which all these datasets will reside. Finally, a modest observing program will be conducted to deepen and expandthe existing spectroscopic and photometric data for known white dwarfs that will fill key gaps as wellas validate the classifications for all stars in the catalog and database.The database contents (both observed and parameters derived through this work) willbe made publicly available via the internet and used by astronomers across several disciplines(Galactic structure, formation, compact object physics, etc.). Multiple undergraduate students willbe involved with aspects of the work at both Arizona and Florida Tech. They will participatein the design of the database, its population, and analysis and will have the opportunity to experiencescientific research with large data sets.