MilkyWay@home: Harnessing volunteer computers to constrain dark matter in the Milky Way

MilkyWay@home is a volunteer computing project that allows people from every country in the world to volunteer their otherwise idle processors to Milky Way research. Currently, more than 25,000 people (150,000 since November 9, 2007) contribute about half a PetaFLOPS of computing power to our project. We currently run two types of applications: one application fits the spatial density profile of tidal streams using statistical photometric parallax, and the other application finds the N-body simulation parameters that produce tidal streams that best match the measured density profile of known tidal streams. The stream fitting application is well developed and is producing published results. The Sagittarius dwarf leading tidal tail has been fit, and the algorithm is currently running on the trailing tidal tail and bifurcated pieces. We will soon have a self-consistent model for the density of the smooth component of the stellar halo and the largest tidal streams. The $N$-body application has been implemented for fitting dwarf galaxy progenitor properties only, and is in the testing stages. We use an Earth-Mover Distance method to measure goodness-of-fit for density of stars along the tidal stream. We will add additional spatial dimensions as well as kinematic measures in a piecemeal fashion, with the eventual goal of fitting the orbit and parameters of the Milky Way potential (and thus the density distribution of dark matter) using multiple tidal streams.

Determining distances to stars statistically from photometry

In determining the distances to stars within the Milky Way galaxy, one often uses photometric or spectroscopic parallax. In these methods, the type of each individual star is determined, and the absolute magnitude of that star type is compared with the measured apparent magnitude to determine individual distances. In this article, we define the term statistical photometric parallax, in which statistical knowledge of the absolute magnitudes of stellar populations is used to determine the underlying density distributions of those stars. This technique has been used to determine the density distribution of the Milky Way stellar halo and its component tidal streams, using very large samples of stars from the Sloan Digital Sky Survey. Most recently, the volunteer computing platform MilkyWay@home has been used to find the best fit model parameters for the density of these halo stars.

Census of blue stars in SDSS DR8

We present a census of the 12,060 spectra of blue objects ($(g-r)_0<-0.25$) in the Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8). As part of the data release, all of the spectra were cross-correlated with 48 template spectra of stars, galaxies and QSOs to determine the best match. We compared the blue spectra by eye to the templates assigned in SDSS DR8. 10,856 of the objects matched their assigned template, 170 could not be classified due to low signal-to-noise (S/N), and 1034 were given new classifications. We identify 7458 DA white dwarfs, 1145 DB white dwarfs, 273 rarer white dwarfs (including carbon, DZ, DQ, and magnetic), 294 subdwarf O stars, 648 subdwarf B stars, 679 blue horizontal branch stars, 1026 blue stragglers, 13 cataclysmic variables, 129 white dwarf - M dwarf binaries, 36 objects with spectra similar to DO white dwarfs, 179 QSOs, and 10 galaxies. We provide two tables of these objects, sample spectra that match the templates, figures showing all of the spectra that were grouped by eye, and diagnostic plots that show the positions, colors, apparent magnitudes, proper motions, etc. for each classification. Future surveys will be able to use templates similar to stars in each of the classes we identify to classify blue stars, including rare types, automatically.

Effect of Night Laboratories on Learning Objectives for a Non-Major Astronomy Class

We tested the effectiveness on learning of hands-on, night-time laboratories that challenged student misconceptions in a non-major introductory astronomy class at Rensselaer Polytechnic Institute. We present a new assessment examination used to assess learning in this study. We were able to increase learning, at the 8.0 sigma level, on one of the moon phase objectives that was addressed in a cloudy night activity. There is weak evidence of some improvement on a broader range of learning objectives. We show evidence that the overall achievement levels of the four sections of the class is correlated with the amount of clear whether the sections had for observing, even though the learning objectives were addressed primarily in activities that did not require clear skies. This last result should be confirmed with future studies. We describe our first attempt to cycle the students through different activity stations in an attempt to handle 18 students at a time in the laboratories, and lessons learned from this.