Precision of the Chinese Space Station Telescope (CSST) stellar radial velocities

Sun, Yuejin; Deng, Dingshan; Yuan, Haibo

doi:10.1088/1674-4527/21/4/92

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…As shown, most of the outliers were images with defects. This demonstrates the ability of the VAE dimensionality reduction method to quickly and effectively detect defected images in modern and future image surveys, such as the CSST optical survey (Sun et al 2021).…”

Section: Conclusion and Discussionmentioning

confidence: 82%

From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation

Xu,

Shen,

de Souza

et al. 2023

Preprint

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We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational autoencoders (VAE) and domain adaptation (DA). We demonstrate the effectiveness of this approach using a sample of low redshift galaxies with detailed morphological type labels from the Galaxy-Zoo DECaLS project. We show that 40-dimensional latent variables can effectively reproduce most morphological features in galaxy images. To further validate the effectiveness of our approach, we utilised a classical random forest (RF) classifier on the 40-dimensional latent variables to make detailed morphology feature classifications. This approach performs similarly to a direct neural network application on galaxy images. We further enhance our model by tuning the VAE network via DA using galaxies in the overlapping footprint of DECaLS and BASS+MzLS, enabling the unbiased application of our model to galaxy images in both surveys. We observed that noise suppression during DA led to even better morphological feature extraction and classification performance. Overall, this combination of VAE and DA can be applied to achieve image dimensionality reduction, defect image identification, and morphology classification in large optical surveys.

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Section: Conclusion and Discussionmentioning

confidence: 82%

From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation

Xu,

Shen,

de Souza

et al. 2023

Preprint

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“…Our method can be applied directly to the Mephisto data once it is available. The algorithm can also be applied to the data of other time-domain surveys, such as the Zwicky Transient Facility (ZTF; Mahabal et al 2019;Graham et al 2019;Bellm et al 2019), Wide Field Survey Telescope (WFST; Chen et al 2019;Lou et al 2020), LSST and China Space Station Telescope (CSST; Zhao et al 2016;Yuan et al 2021;Sun et al 2021;Cao et al 2021a;Cao et al 2021b).…”

Section: Discussionmentioning

confidence: 99%

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

Lei¹,

Chen²,

Li³

et al. 2022

Res. Astron. Astrophys.

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We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~ 150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. We have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

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“…(ii) The CSST can observe a large number of these stars during a short time thanks to its large FOV. (iii) A narrow segment of CSST spectra can provide reliable estimates of radial velocities (Sun et al 2021). Taking advantage of the above prerequisites, the key idea of this method is to use enormous numbers of stars (absorption lines rather than emission lines) of known radial velocities observed during normal scientific observations as wavelength standards to monitor and correct for possible errors in wavelength calibration.…”

Section: Introductionmentioning

confidence: 99%

A Star-based Method for the Precise Flux Calibration of the Chinese Space Station Telescope Slitless Spectroscopic Survey

Yang,

Yuan,

Duan

et al. 2024

ApJS

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The upcoming Chinese Space Station Telescope (CSST) slitless spectroscopic survey poses a challenge of flux calibration, which requires a large number of flux-standard stars. In this work, we design an uncertainty-aware residual attention network, the UaRA-net, to derive the CSST spectral energy distributions (SEDs) with a resolution of R = 200 over the wavelength range of 2500–10000 Å using LAMOST normalized spectra with a resolution of R = 2000 over the wavelength range of 4000–7000 Å. With the special structure and training strategy, the proposed model provides accurate predictions not only of SEDs, but also of their corresponding errors. The precision of the predicted SEDs depends on the effective temperature (T eff), wavelength, and the LAMOST spectral signal-to-noise ratios (S/Ns), particularly in the GU band. For stars with T eff = 6000 K, the typical SED precisions in the GU band are 4.2%, 2.1%, and 1.5% at S/N values of 20, 40, and 80, respectively. As T eff increases to 8000 K, the precision increases to 1.2%, 0.6%, and 0.5%, respectively. The precision is higher at redder wavelengths. In the GI band, the typical SED precisions for stars with T eff = 6000 K increase to 0.3%, 0.1%, and 0.1% at S/N values of 20, 40, and 80, respectively. We further verify our model using empirical MILES spectra and find a good performance. The proposed method will open up new possibilities for the optimal use of slitless spectra of the CSST and other surveys.

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Precision of the Chinese Space Station Telescope (CSST) stellar radial velocities

Cited by 13 publications

References 14 publications

From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation

From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

A Star-based Method for the Precise Flux Calibration of the Chinese Space Station Telescope Slitless Spectroscopic Survey

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