Characterizing the Instrumental Profile of LAMOST

Abstract

The instrumental profile (IP) of a telescope is of great significance for spectroscopic analyses, especially for wavelength calibration and stellar parameter measurements. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) employs arc lamps for wavelength calibration. These lamps produce sharp emission lines with known wavelengths, and the observed arc lamp spectra can well characterize the IP. However, IPs are influenced by multiple factors, making them difficult to model accurately with traditional methods. Neural networks, which can automatically capture complex patterns and nonlinear features in data, provide a promising approach for high-precision IP measurement. We therefore construct a multi-layer perceptron (MLP) based on The Payne neural network to derive IPs for LAMOST. After training, the model can retrieve the IP for any fiber, at any wavelength, and at any time. We then apply the derived IP to stellar radial velocity (RV) measurements and analyze the impact of different IP center localization methods on the results. Finally, the dispersion of the measured RVs is reduced by approximately 3 km/s. This improvement will facilitate the search for long-period binary stars via RV variations.

Figures (10)

• Figure 1: This figure shows the variation of the lamp line profile parameters for LAMOST Spectrograph No.1 at a given time (indicated in the title of each panel). From top to bottom, the panels show two-dimensional maps of the FWHM, skewness, and kurtosis of the profiles, with fiber ID on the x-axis and wavelength on the y-axis.
• Figure 2: Arc lamp spectra used for wavelength calibration of LAMOST low-resolution data. Panel (a) presents an exposure of the Hg–Cd arc lamp used for the blue arm, while panel (b) shows an example of the Ne–Ar arc lamp spectrum obtained for the red arm. The red points mark the peaks of the selected emission lines, with their central wavelengths labeled (in units of Å) adjacent to each point.
• Figure 3: One example of the training and validation loss curves in the year of 2024. Colors of the lines indicate the 16 spectrographs of LAMOST. "blue" and "red" mean the corresponding bands. Solid lines represent the training loss, while dashed lines of the same color correspond to the validation loss.
• Figure 4: The left and right panels show the distributions of $\mathrm{mean}_{\mathrm{res}}$ and $\sigma_{\mathrm{res}}$ for all samples, respectively. The color of the lines indicates the spectral band, and the dashed lines represent the mean value.
• Figure 5: Comparison between the original and neural network predicted IP for three adjacent fibers (fiber 154–156). The top panels show the normalized flux profiles, where the blue lines represent the original IP and the orange dashed lines show the predicted IP. The bottom panels show the residuals (original – predicted), with the red dashed lines indicating zero. The middle panel (fiber 155) exhibits significantly larger residuals compared to its neighbors, suggesting a possible abrupt change in the IP shape for that fiber. This deviation likely explains the relatively poor fitting performance for fiber 155.