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Photometric stellar surveys now cover a large fraction of the sky, probe to fainter magnitudes than large-scale spectroscopic surveys, and are relatively free from the target selection biases often associated with such studies. Photometric-metallicity estimates that include narrow/medium-band filters can achieve comparable accuracy and precision to existing low-resolution spectroscopic surveys such as Sloan Digital Sky Survey/SEGUE and LAMOST. Here we report on an effort to identify likely members of the Galactic disk system among the very metal-poor (VMP; [Fe/H] ≤ −2) and extremely metal-poor (EMP; [Fe/H] ≤ −3) stars. Our analysis is based on an initial sample of ∼11.5 million stars with full space motions selected from the SkyMapper Southern Survey (SMSS) and Stellar Abundance and Galactic Evolution Survey (SAGES). After applying a number of quality cuts to obtain the best available metallicity and dynamical estimates, we analyze a total of ∼5.86 million stars in the combined SMSS/SAGES sample. We employ two techniques that, depending on the method, identify between 876 and 1476 VMP stars (6.9%−11.7% of all VMP stars) and between 40 and 59 EMP stars (12.4%−18.3% of all EMP stars) that appear to be members of the Galactic disk system on highly prograde orbits ( v _ϕ > 150 km s ^−1 ). The total number of candidate VMP/EMP disklike stars is 1496, the majority of which have low orbital eccentricities, ecc ≤ 0.4; many have ecc ≤ 0.2. The large fractions of VMP/EMP stars associated with the Milky Way disk system strongly suggest the presence of an early-forming “primordial” disk.

Utilizing high-cadence and continuous g - and r -band data over three nights acquired from the 3.6 m Canada–France–Hawaii Telescope, aiming to find short-duration microlensing events, we conduct a systematic search for variables, transients, and asteroids across a ∼1° field of view of the Andromeda galaxy (M31). We present a catalog of 5859 variable stars, yielding the most extensive compilation of short-period variable sources of M31. We also detected 19 flares, predominantly associated with foreground M dwarfs in the Milky Way. In addition, we discovered 17 previously unknown asteroid candidates, and we subsequently reported them to the Minor Planet Center. Lastly, we report a microlensing event candidate C-ML-1 and present a preliminary analysis.

We present a comprehensive recalibration of narrowband/medium-band and broadband photometry from the Southern Photometric Local Universe Survey (S-PLUS) by leveraging two approaches: an improved Gaia XP synthetic photometry (XPSP) method with corrected Gaia XP spectra, and the stellar color regression (SCR) method with corrected Gaia Early Data Release 3 photometric data and spectroscopic data from LAMOST Data Release 7. Through the use of millions of stars as standards per band, we demonstrate the existence of position-dependent systematic errors, up to 23 mmag for the main survey region, in the S-PLUS iDR4 photometric data. A comparison between the XPSP and SCR methods reveals minor differences in zero-point offsets, typically within the range of 1–6 mmag, indicating the accuracy of the recalibration, and a twofold to threefold improvement in the zero-point precision. During this process, we also verify and correct for systematic errors related to CCD position. The corrected S-PLUS iDR4 photometric data will provide a solid data foundation for conducting scientific research that relies on high-precision calibration. Our results underscore the power of the XPSP method in combination with the SCR method, showcasing their effectiveness in enhancing calibration precision for wide-field surveys when combined with Gaia photometry and XP spectra, to be applied for other S-PLUS subsurveys.

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.

We combine photometric data from GALEX GR6+7 All-Sky Imaging Survey and Gaia Early Data Release 3 with stellar parameters from the SAGA and PASTEL catalogs to construct high-quality training samples for dwarfs (0.4 < BP − RP < 1.6) and giants (0.6 < BP − RP < 1.6). We apply careful reddening corrections using empirical temperature- and extinction-dependent extinction coefficients. Using the two samples, we establish a relationship between stellar loci (near-ultraviolet (NUV)−BP versus BP − RP colors), metallicity, and M _G . For a given BP − RP color, a 1 dex change in [Fe/H] corresponds to an approximately 1 magnitude change in NUV − BP color for solar-type stars. These relationships are employed to estimate metallicities based on NUV − BP, BP − RP, and M _G . Thanks to the strong metallicity dependence in the GALEX NUV band, our models enable a typical photometric-metallicity precision of approximately σ _[Fe/H] = 0.11 dex for dwarfs and σ _[Fe/H] = 0.17 dex for giants, with an effective metallicity range extending down to [Fe/H] = −3.0 for dwarfs and [Fe/H] = −4.0 for giants. We also find that the NUV-band-based photometric-metallicity estimate is not as strongly affected by carbon enhancement as previous photometric techniques. With the GALEX and Gaia data, we have estimated metallicities for about 5 million stars across almost the entire sky, including approximately 4.5 million dwarfs and 0.5 million giants. This work demonstrates the potential of the NUV band for estimating photometric metallicities, and sets the groundwork for utilizing the NUV data from space telescopes such as the upcoming Chinese Space Station Telescope.

By combining spectra from the CALSPEC and NGSL, as well as spectroscopic data from the LAMOST Data Release 7 (DR7), we have analyzed and corrected the systematic errors of the Gaia DR3 BP/RP (XP) spectra. The errors depend on the normalized spectral energy distribution (simplified by two independent “colors”) and G magnitude. Our corrections are applicable in the range of approximately −0.5 < BP − RP < 2, 3 < G < 17.5, and E ( B − V ) < 0.8. To validate our correction, we conduct independent tests by comparisons with the MILES and LEMONY spectra. The results demonstrate that the systematic errors of BP − RP and G have been effectively corrected, especially in the near-ultraviolet. The consistency between the corrected Gaia XP spectra and the MILES and LEMONY is better than 2% in the wavelength range of 336–400 nm and 1% in redder wavelengths. A global absolute calibration is also carried out by comparing the synthetic Gaia photometry from the corrected XP spectra with the corrected Gaia DR3 photometry. Our study opens up new possibilities for using XP spectra in many fields. A Python package is publicly available to do the corrections (doi: https://doi.org/10.12149/101375 or https://github.com/HiromonGON/GaiaXPcorrection ).

We employ the corrected Gaia Early Data Release 3 photometric data and spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR7 to assemble a sample of approximately 0.25 million FGK dwarf photometric standard stars for the 12 J-PLUS filters using the stellar color regression (SCR) method. We then independently validate the J-PLUS DR3 photometry and uncover significant systematic errors: up to 15 mmag in the results from the stellar locus method and up to 10 mmag primarily caused by magnitude-, color-, and extinction-dependent errors of the Gaia XP spectra as revealed by the Gaia BP/RP (XP) synthetic photometry (XPSP) method. We have also further developed the XPSP method using the corrected Gaia XP spectra by B. Huang et al. and applied it to the J-PLUS DR3 photometry. This resulted in an agreement of 1–5 mmag with the SCR method and a twofold improvement in the J-PLUS zero-point precision. Finally, the zero-point calibration for around 91% of the tiles within the LAMOST observation footprint is determined through the SCR method, with the remaining approximately 9% of the tiles outside this footprint relying on the improved XPSP method. The recalibrated J-PLUS DR3 photometric data establish a solid data foundation for conducting research that depends on high-precision photometric calibration.

The total-to-selective extinction ratio, R _V , is a key parameter for tracing the properties of interstellar dust, as it directly determines the variation of the extinction curve with wavelength. By utilizing accurate color excess measurements from the optical to the mid-infrared range, we have derived R _V values for approximately three million stars from LAMOST data release 7 using a forward-modeling technique. This extensive data set enables us to construct a comprehensive 2D R _V map of the Milky Way within the LAMOST footprint at a spatial resolution of ∼27.′5. Based on reliable sight lines of E ( B − V ) > 0.1, we find that R _V exhibits a Gaussian distribution centered around 3.25 with a standard deviation of 0.25. The spatial variability of R _V in the Galactic disk exhibits a wide range, spanning from small scales within individual molecular clouds to large scales up to kiloparsecs. A striking correlation is observed between the distribution of R _V and molecular clouds. Notably, we observe lower R _V values within the regions of nearby molecular clouds compared to their surrounding areas. Furthermore, we have investigated the relationships between R _V and various parameters, including dust temperature, dust emissivity spectral index, column densities and ratios of atomic and molecular hydrogen, as well as the gas-to-dust ratio. We find that these relationships vary with the level of extinction. These analyses provide new insights into the properties and evolution of dust grains in diverse interstellar environments and also hold significant importance for achieving accurate extinction corrections.

In this work, we perform the recalibration of Pan-STARRS1 (PS1) photometry by correcting for position-dependent systematic errors using the spectroscopy-based stellar color regression (SCR) method, the photometry-based SCR method, and the Gaia BP/RP synthetic photometry method. We confirm the significant large-scale and small-scale spatial variation of the magnitude offsets for all the grizy filters. We show that the PS1 photometric calibration precisions in the grizy filters are around 5–7 mmag when averaged over 14′ regions. We note a much larger calibration error up to 0.04 mag in the Galactic plane, which is probably caused by the systematic errors of the PS1 magnitudes in crowded fields. The results of the three methods are consistent with each other within 1–2 mmag or better for all the filters. We provide 2D maps and a Python package to correct for position-dependent magnitude offsets of PS1, which can be used for high-precision investigations and as a reference to calibrate other surveys.

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 deg ^2 . 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.

We have obtained accurate dust reddening from the far-ultraviolet to the mid-infrared for up to 5 million stars by the star-pair algorithm based on LAMOST stellar parameters along with Galaxy Evolution Explorer, Pan-STARRS 1, Gaia, Sloan Digital Sky Survey, Two Micron All Sky Survey, and Wide-field Infrared Survey Explorer photometric data. The typical errors are between 0.01 and 0.03 mag for most colors. We derived the empirical reddening coefficients for 21 colors both in the traditional (single-valued) way and as a function of T _eff and E ( B − V ) by using the largest samples of accurate reddening measurements, together with the extinction values from Schlegel et al. The corresponding extinction coefficients have also been obtained. The results are compared with model predictions and generally in good agreement. Comparisons with measurements in the literature show that the T _eff - and E ( B − V )-dependent coefficients explain the discrepancies between different measurements naturally, i.e., using sample stars of different temperatures and reddening. Our coefficients are mostly valid in the extinction range of 0–0.5 mag and the temperature range of 4000–10,000 K. We recommend that the new T _eff - and E ( B − V )-dependent reddening and extinction coefficients should be used in the future. A Python package is also provided for the usage of the coefficients ( https://github.com/vnohhf/extinction_coeffcient/ ).