Evaluation for Faithful Reproduction of Star Fields in a Planetarium

2018 ◽  
Vol 2018 (16) ◽  
pp. 060401-1-060401-12
Author(s):  
Midori Tanaka ◽  
Takahiko Horiuchi ◽  
Ken'ichi Otani ◽  
Po-Chieh Hung
Keyword(s):  
1971 ◽  
Vol 11 ◽  
pp. 194-197
Author(s):  
W. A. Deutschman

During the 15 months that we operated the Celescope experiment on the Orbiting Astronomical Observatory, we acquired 8700 television frames of star fields containing a total of approximately 10 000 stars.Figure 1 shows an example of one of these frames. Note the target ring, an aluminum deposit on the target of the tube, in each corner of the picture; the shadow in the upper left corner; the calibration lamp just below the center of the picture; and its ghost slightly below and to the left. All other objects are stars. Each frame consists of 256 scan lines designated by the number k, with each line containing 251 pixels (picture elements) designated by the numberl, making a total of 65 000 intensity points I(k,l). The frames are divided into two spectral regions by two filters—lines 1 to 128 have one spectral range, lines 129 to 256 have a different one. Hence, each frame is reduced as two half frames in the first portion of the reduction system.


1970 ◽  
Vol 36 ◽  
pp. 109-119
Author(s):  
Robert J. Davis

We have used the television photometers in the Celescope OAO experiment to measure the far ultraviolet brightnesses of several thousand stars, including parts of the constellations Draco, Lyra, Puppis, Vela, Taurus, and Orion; and the Moon. As of this date (22 July 1969), three of our four cameras continue to operate satisfactorily, and we are making measurements in additional star fields distributed throughout the sky. Our shortest wavelength band, which includes the Lyman α line of atomic hydrogen, provides information on the Earth's outer atmosphere, as well as on star brightnesses. The intensity of the Lyman α radiation from the geocorona is a maximum when the Sun is near the horizon as seen by the OAO, and a minimum when the Sun is in the nadir. The direction that the telescope points does not much affect the measured intensities.Because of the heavy logistic problems of identification, calibration, and measurement for so many stars and because of the survey character of the program, the scientific interpretation of the results is, as expected, lagging the measurement program. However, one consistent picture beginning to emerge from our data is that our observed stars are about equally divided between those that fall within 0.5 magnitude of the predicted ultraviolet brightnesses and those that are significantly fainter than predicted. Most of the giant stars we observe exhibit these ultraviolet deficiencies. Since some of these giants are stars for which little or no interstellar reddening is predicted, we attribute the observed deficiencies to the stars themselves.Many of the objects we observe do not have accurate ground-based published data regarding magnitude, color, and spectral type; new ground-based observations of these objects are required to ensure satisfactory interpretation of our results.


1922 ◽  
Vol 34 ◽  
pp. 148
Author(s):  
Frederick Slocum
Keyword(s):  
The Sun ◽  

1973 ◽  
Vol 78 ◽  
pp. 401 ◽  
Author(s):  
P. A. Wehinger ◽  
B. Hidajat

2019 ◽  
Vol 621 ◽  
pp. A103 ◽  
Author(s):  
J. Bialopetravičius ◽  
D. Narbutis ◽  
V. Vansevičius

Context. Convolutional neural networks (CNNs) have been proven to perform fast classification and detection on natural images and have the potential to infer astrophysical parameters on the exponentially increasing amount of sky-survey imaging data. The inference pipeline can be trained either from real human-annotated data or simulated mock observations. Until now, star cluster analysis was based on integral or individual resolved stellar photometry. This limits the amount of information that can be extracted from cluster images. Aims. We aim to develop a CNN-based algorithm capable of simultaneously deriving ages, masses, and sizes of star clusters directly from multi-band images. We also aim to demonstrate CNN capabilities on low-mass semi-resolved star clusters in a low-signal-to-noise-ratio regime. Methods. A CNN was constructed based on the deep residual network (ResNet) architecture and trained on simulated images of star clusters with various ages, masses, and sizes. To provide realistic backgrounds, M 31 star fields taken from The Panchromatic Hubble Andromeda Treasury (PHAT) survey were added to the mock cluster images. Results. The proposed CNN was verified on mock images of artificial clusters and has demonstrated high precision and no significant bias for clusters of ages ≲3 Gyr and masses between 250 and 4000 M⊙. The pipeline is end-to-end, starting from input images all the way to the inferred parameters; no hand-coded steps have to be performed: estimates of parameters are provided by the neural network in one inferential step from raw images.


2012 ◽  
Vol 21 (3) ◽  
Author(s):  
S. V. Vereshchagin ◽  
N. V. Chupina

AbstractThe plate collection of the Zvenigorod 40-cm Carl Zeiss astrograph, obtained in 1972-2003, contains direct photographs of star fields, comets, asteroids, Pluto, and Mars. The electronic library of images from photographic plates was created from scanning the astronomical negatives. We present information on programs scheduled at the telescope and the structure and maintenance of the plate stacks. We also list the plates with images of asteroids and comets. Access to all our plate lists is provided at the web sites of the Institute of Astronomy (INASAN) and WFPDB. It is possible to select plates by the date of observation, by the coordinates of the sky area, by the object type. Preview images can be inspected.


1958 ◽  
Vol 8 ◽  
pp. 951-952
Author(s):  
V. G. Fessenkov
Keyword(s):  

When investigating sufficiently dense star fields, one often finds some short chains of stars oriented with a certain regularity and in a few cases connected with dark filaments of the galactic nebulae. It is necessary to be very cautious in the interpretation of this phenomenon, because even completely accidental clustering of some objects can sometimes lead to interesting deviations from homogeneity.


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