Test area of the SAGE survey

Zheng, Jie; Zhao, Gang; Wang, W.; Zhou, Fan; Tan, Kefeng; Li, Chun; Zuo, Fang

doi:10.1088/1674-4527/19/1/3

Cited by 9 publications

(11 citation statements)

References 10 publications

(3 reference statements)

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“…Table 2 shows the configuration parameters for SCAMP in our pipeline for the two runs. It is found that DISTORT DEGREES = 3 is the most proper configuration after a series of tests (Zheng et al 2018(Zheng et al , 2019.…”

Section: Astrometrymentioning

confidence: 95%

“…We constructed the combined master bias frame every night by using the median value of the 20 exposures at the same pixel. Thus both the science images and the flat-field images will be corrected for bias by applying this master bias image, which are not only corrected for the mean value of bias but also for the structure of bias (Zheng et al 2018(Zheng et al , 2019.…”

Section: Image Processingmentioning

confidence: 99%

“…The reference stars are distributed across the whole sky with the astrometric accuracy of ∼0 02 in both R.A. and decl. (Zheng et al 2018(Zheng et al , 2019.…”

Section: Astrometrymentioning

confidence: 99%

“…The recent Stellar Abundances and Galactic Evolution Survey (SAGES; PI: Gang Zhao, Fan et al 2018;Zheng et al 2018Zheng et al , 2019 is an excellent survey for such a purpose. The survey aims to derive stellar atmospheric parameters for a few hundred million stars in the u s /v s /g/r/i/α n /α w /DDO51 bands.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Stellar Abundances and Galactic Evolution Survey (SAGES). I. General Description and the First Data Release (DR1)

Fan,

Zhao,

Wang

et al. 2023

ApJS

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The Stellar Abundances and Galactic Evolution Survey (SAGES) of the northern sky is a specifically designed multiband photometric survey aiming to provide reliable stellar parameters with accuracy comparable to those from low-resolution optical spectra. It was carried out with the 2.3 m Bok telescope of Steward Observatory and three other telescopes. The observations in the u s and v s passband produced over 36,092 frames of images in total, covering a sky area of ∼9960 deg2. The median survey completenesses of all observing fields for the two bands are u s = 20.4 mag and v s = 20.3 mag, respectively, while the limiting magnitudes with signal-to-noise ratio of 100 are u s ∼ 17 mag and v s ∼ 18 mag, correspondingly. We combined our catalog with the data release 1 (DR1) of the first Panoramic Survey Telescope And Rapid Response System (Pan-STARRS, PS1) catalog, and obtained a total of 48,553,987 sources that have at least one photometric measurement in each of the SAGES u s and v s and PS1 grizy passbands. This is the DR1 of SAGES, released in this paper. We compared our gri point-source photometry with those of PS1 and found an rms scatter of ∼2% difference between PS1 and SAGES for the same band. We estimated an internal photometric precision of SAGES to be of the order of ∼1%. Astrometric precision is better than 0.″2 based on comparison with DR1 of the Gaia mission. In this paper, we also describe the final end-user database, and provide some science applications.

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Section: Astrometrymentioning

confidence: 95%

Section: Image Processingmentioning

confidence: 99%

“…The reference stars are distributed across the whole sky with the astrometric accuracy of ∼0 02 in both R.A. and decl. (Zheng et al 2018(Zheng et al , 2019.…”

Section: Astrometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Stellar Abundances and Galactic Evolution Survey (SAGES). I. General Description and the First Data Release (DR1)

Fan,

Zhao,

Wang

et al. 2023

ApJS

View full text Add to dashboard Cite

show abstract

“…According to the needs of different data reduction, the corresponding pipeline has been completed. The main scientific projects conducted using NOWT after the installation have been multi-color photometry of binaries (Zhang et al 2018), pulsating stars (Fu et al 2017), exoplanet (Wang et al 2017), variable stars (Yang et al 2018, Bai et al 2019, gravitational wave ( Liu & Zhang 2014), galaxies (Zhang et al 2015), survey (Ma et al 2018, Zheng et al 2019.…”

Section: Introductionmentioning

confidence: 99%

The Wide-field Photometric System of the Nanshan One-meter Telescope

Bai,

Feng,

Zhang

et al. 2020

Preprint

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The Nanshan One-meter Wide-field Telescope (NOWT) is a prime focus system located at Nanshan Station of Xinjiang Astronomical Observatories (XAO). The field of view(FOV) was designed to 1.5 • × 1.5 • , and Johnson-Cousins UBVRI system was chosen as the main Filter set. The telescope has been providing observation services for astronomers since Sept. 2013. Variable source searching and time-domain surveys are the main scientific goals. The system's test results are reported including linearity, dark current, bias, readout noise and gain of the CCD camera. The accurate instrumental calibration coefficients in UBVRI bands was driven with Landolt standard stars during photometric nights. Finally, the limiting magnitudes are given with signal-to-noise ratios and various exposure times for observers.

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Testing Deep Learning for Deriving Stellar Atmospheric Parameters with Extended MILES Library

Wang

Zhao

Zhang

et al. 2019

PASP

Self Cite

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The stellar atmospheric parameters T eff , g log , and [Fe/H] are important physical parameters. However, there are different ways to derive them using both spectroscopy and photometry. In this work, the extended MILES library has been convolved with the uvgri-band transmission of the Stellar Abundance and Galaxy Evolution Survey (SAGES), which aims to derive the stellar atmospheric parameters of 0.5 billion stars from observations. With the convolved SAGES magnitudes and MILES library, we examine the errors and stability of deep learning for deriving the stellar atmospheric parameters. The basic idea of our approach is to take photometric data such as images and train a deep neural network (DNN) with densely connected convolutional structure, which is widely used in the field of computer vision. Using this technique, stellar atmospheric parameters can be derived. Increasing the depth of the DNN makes it more difficult to train the DNN, but the dense connected convolutional structure can be a good solution to the gradient dispersion. Finally, we evaluate the performance of the proposed scheme with photometric data. Our method, trained by photometric data of only 483 stars, reduces the rms of T eff , g log and [Fe/H] to 74 K, 0.11 dex and 0.06 dex, respectively in training and 110 K, 0.289 dex and 0.211 dex, respectively, in testing. It is worth noting that our method can directly calculate T eff , g log and [Fe/H] continuously in range of ∼4000-7000 K, ∼2.0-5.0 dex and ∼−2.0-0.5 dex, respectively, rather than piecewise calculating the stellar atmospheric parameters in different stellar types and colors.

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Test area of the SAGE survey

Cited by 9 publications

References 10 publications

The Stellar Abundances and Galactic Evolution Survey (SAGES). I. General Description and the First Data Release (DR1)

The Stellar Abundances and Galactic Evolution Survey (SAGES). I. General Description and the First Data Release (DR1)

The Wide-field Photometric System of the Nanshan One-meter Telescope

Testing Deep Learning for Deriving Stellar Atmospheric Parameters with Extended MILES Library

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