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The upcoming Chinese Space Station Telescope (CSST) slitless spectroscopicsurvey poses a challenge of flux calibration, which requires a large number offlux-standard stars. In this work, we design an uncertainty-aware residualattention network, the UaRA-net, to derive the CSST SEDs with a resolution of R= 200 over the wavelength range of 2500-10000 \AA using LAMOST normalizedspectra with a resolution of R = 2000 over the wavelength range of 4000-7000\AA. With the special structure and training strategy, the proposed model cannot only provide accurate predictions of SEDs but also their correspondingerrors. The precision of the predicted SEDs depends on effective temperature(Teff), wavelength, and the LAMOST spectral signal-to-noise ratios (SNRs),particularly in the GU band. For stars with Teff = 6000 K, the typical SEDprecisions in the GU band are 4.2%, 2.1%, and 1.5% at SNR values of 20, 40, and80, respectively. As Teff increases to 8000 K, the precision increases to 1.2%,0.6%, and 0.5%, respectively. The precision is higher at redder wavelengths. Inthe GI band, the typical SED precisions for stars with Teff = 6000 K increaseto 0.3%, 0.1%, and 0.1% at SNR values of 20, 40, and 80, respectively. Wefurther verify our model using the empirical spectra of the MILES and find goodperformance. The proposed method will open up new possibilities for optimalutilization of slitless spectra of the CSST and other surveys.

A star-based method for precise flux calibration of the Chinese Space Station Telescope (CSST) slitless spectroscopic survey