239 research outputs found
Do the nearby BHB stars belong to the Thick Disk or the Halo?
We study the Milky Way region Z<3.0 kpc, where the thick disk and inner halo
overlap, by using the kinematics of local blue horizontal branch (BHB) stars
(within 1 kpc) and new samples of BHB stars and A-type stars from the Century
Survey. We derive Galactic U,V,W velocities for these BHB and A-type star
samples using proper motions from the NOMAD catalog. The mean velocities and
the velocity dispersions of the BHB samples (Z<3 kpc) are characteristic of the
halo, while those of the Century Survey A-type stars are characteristic of the
thick disk. There is no evidence from our samples that the BHB stars rotate
with the thick disk in the region Z<3 kpc. Nearly a third of the nearby local
RR Lyrae stars have disk kinematics and are more metal-rich than [Fe/H]~-1.
Only a few percent of the Century Survey BHB stars have these properties. Only
one nearby BHB star (HD 130201) is likely to be such a disk star but selection
based on high proper motions will have tended to exclude such stars from the
local sample. The scale height derived from a sample of local RR Lyrae stars
agrees with that of the Century Survey BHB stars. The local samples of BHB
stars and metal-weak red giants are too incomplete for a similar comparison.Comment: 14 pages, accepted to A
A Preliminary Discussion of the Kinematics of BHB and RR Lyrae Stars near the North Galactic Pole
The radial velocity dispersion of 67 RR Lyrae variable and blue horizontal
branch (BHB) stars that are more than 4 kpc above the galactic plane at the
North Galactic Pole is 110 km/sec and shows no trend with Z (the height above
the galactic plane). Nine stars with Z < 4 kpc show a smaller velocity
dispersion (40 +/-9 km/sec) as is to be expected if they mostly belong to a
population with a flatter distribution. Both RR Lyrae stars and BHB stars show
evidence of stream motion; the most significant is in fields RR2 and RR3 where
24 stars in the range 4.0 < Z < 11.0 kpc have a mean radial velocity of -59 +/-
16 km/sec. Three halo stars in field RR 2 appear to be part of a moving group
with a common radial velocity of -90 km/sec. The streaming phenomenon therefore
occurs over a range of spatial scales. The BHB and RR Lyrae stars in our sample
both have a similar range of metallicity (-1.2 < [Fe/H] < -2.2). Proper motions
of BHB stars in fields SA 57 (NGP) and the Anticenter field (RR 7) (both of
which lie close to the meridional plane of the Galaxy) show that the stars that
have Z 4 kpc have a Galactic V motion that is
< -200 km/sec and which is characteristic of the halo. Thus the stars that have
a flatter distribution are really halo stars and not members of the metal-weak
thick-disk.Comment: Accepted for publication in the March 1996 AJ. 15 pages, AASTeX V4.0
latex format (including figures), 2 eps figures, 2 separate AASTeX V4.0 latex
table
Structural Parameters and Dynamical Masses for Globular Clusters in M33
Using high-dispersion spectra from the HIRES echelle spectrograph on the Keck
I telescope, we measure velocity dispersions for 4 globular clusters in M33.
Combining the velocity dispersions with integrated photometry and structural
parameters derived from King-Michie model fits to WFPC2 images, we obtain
mass-to-light ratios for the clusters. The mean value is M/LV = 1.53 +/- 0.18,
very similar to the M/LV of Milky Way and M31 globular clusters. The M33
clusters also fit very well onto the fundamental plane and binding energy -
luminosity relations derived for Milky Way GCs. Dynamically and structurally,
the four M33 clusters studied here appear virtually identical to Milky Way and
M31 GCs.Comment: 25 pages, including 7 figures and 4 tables. Accepted for AJ, Nov 200
The Deep Space Network: A Radio Communications Instrument for Deep Space Exploration
The primary purpose of the Deep Space Network (DSN) is to serve as a communications instrument for deep space exploration, providing communications between the spacecraft and the ground facilities. The uplink communications channel provides instructions or commands to the spacecraft. The downlink communications channel provides command verification and spacecraft engineering and science instrument payload data
Photometric identification of blue horizontal branch stars
We investigate the performance of some common machine learning techniques in
identifying BHB stars from photometric data. To train the machine learning
algorithms, we use previously published spectroscopic identifications of BHB
stars from SDSS data. We investigate the performance of three different
techniques, namely k nearest neighbour classification, kernel density
estimation and a support vector machine (SVM). We discuss the performance of
the methods in terms of both completeness and contamination. We discuss the
prospect of trading off these values, achieving lower contamination at the
expense of lower completeness, by adjusting probability thresholds for the
classification. We also discuss the role of prior probabilities in the
classification performance, and we assess via simulations the reliability of
the dataset used for training. Overall it seems that no-prior gives the best
completeness, but adopting a prior lowers the contamination. We find that the
SVM generally delivers the lowest contamination for a given level of
completeness, and so is our method of choice. Finally, we classify a large
sample of SDSS DR7 photometry using the SVM trained on the spectroscopic
sample. We identify 27,074 probable BHB stars out of a sample of 294,652 stars.
We derive photometric parallaxes and demonstrate that our results are
reasonable by comparing to known distances for a selection of globular
clusters. We attach our classifications, including probabilities, as an
electronic table, so that they can be used either directly as a BHB star
catalogue, or as priors to a spectroscopic or other classification method. We
also provide our final models so that they can be directly applied to new data.Comment: To appear in A&A. 19 pages, 22 figures. Tables 7, A3 and A4 available
electronically onlin
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