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TIC 157365951 has been classified as a $\delta$ Scuti type by the International Variable Star Index (VSX). Through the spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and its light curve, we further discovered that it is a binary system. This binary system comprises a red dwarf star and a compact star. Through the spectral energy distribution (SED) fitting, we determined the mass of the red dwarf star as $M_1 = 0.31 \pm 0.01 M_{\odot}$ and its radius as $R_1 = 0.414 \pm 0.004 R_{\odot}$. By fitting the double-peaked H${\rm \alpha}$ emission, we derived the mass ratio of $q = 1.76 \pm 0.04 $, indicating a compact star mass of $M_2 = 0.54 \pm 0.01 M_{\odot}$. Using Phoebe to model the light curve and radial velocity curve for the detached binary system, we obtained a red dwarf star mass of $M_1 = 0.29 \pm 0.02 M_{\odot}$, a radius of $R_1 = 0.39 \pm 0.04 R_{\odot}$, and a Roche-lobe filling factor of $f = 0.995\pm0.129$, which is close to the $f=1$ expected for a semi-detached system. The Phoebe model gives a compact star mass $M_2 = 0.53 \pm 0.05 M_{\odot}$. Constraining the system to be semidetached gives $M_1 = 0.34 \pm 0.02 M_{\odot}$, $R_1 = 0.41 \pm 0.01 R_{\odot}$, and $M_2 = 0.62 \pm 0.03 M_{\odot}$. The consistency of the models is encouraging. The value of the Roche-lobe filling factor suggests that there might be ongoing mass transfer. The compact star mass is as massive as a typical white dwarf.

With the continuous development of large optical surveys, a large number of light curves of late-type contact binary systems (CBs) have been released. Deriving parameters for CBs using the the WD program and the PHOEBE program poses a challenge. Therefore, this study developed a method for rapidly deriving light curves based on the Neural Networks (NN) model combined with the Hamiltonian Monte Carlo (HMC) algorithm (NNHMC). The neural network was employed to establish the mapping relationship between the parameters and the pregenerated light curves by the PHOEBE program, and the HMC algorithm was used to obtain the posterior distribution of the parameters. The NNHMC method was applied to a large contact binary sample from the Catalina Sky Survey, and a total of 19,104 late-type contact binary parameters were derived. Among them, 5172 have an inclination greater than 70 deg and a temperature difference less than 400 K. The obtained results were compared with the previous studies for 30 CBs, and there was an essentially consistent goodness-of-fit (R2) distribution between them. The NNHMC method possesses the capability to simultaneously derive parameters for a vast number of targets. Furthermore, it can provide an extremely efficient tool for rapid derivation of parameters in future sky surveys involving large samples of CBs.

The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up unprecedented opportunities for stellar classification. Specific types of stars, such as early-type emission-line stars and those with stellar winds, can be distinguished by the profiles of their spectral lines. In this paper, we introduce a method based on derivative spectroscopy (DS) designed to detect signals within complex backgrounds and provide a preliminary estimation of curve profiles. This method exhibits a unique advantage in identifying weak signals and unusual spectral line profiles when compared to other popular line detection methods. We validated our approach using synthesis spectra, demonstrating that DS can detect emission signals three times fainter than Gaussian fitting. Furthermore, we applied our method to 579,680 co-added spectra from LAMOST Medium-Resolution Spectroscopic Survey, identifying 16,629 spectra with emission peaks around the H{\alpha} line from 10,963 stars. These spectra were classified into three distinct morphological groups, resulting in nine subclasses as follows. (1) Emission peak above the pseudo-continuum line (single peak, double peaks, emission peak situated within an absorption line, P Cygni profile, Inverse P Cygni profile); (2) Emission peak below the pseudo-continuum line (sharp emission peak, double absorption peaks, emission peak shifted to one side of the absorption line); (3) Emission peak between the pseudo-continuum line.

Massive binary stars play a crucial role in many astrophysical fields. Investigating the statistical properties of massive binary stars is essential to trace the formation of massive stars and constrain the evolution of stellar populations. However, no consensus has been achieved on the statistical properties of massive binary stars, mainly due to the lack of a large and homogeneous sample of spectroscopic observations. We study the intrinsic binary fraction $f_{\rm b}^{\rm in}$ and distributions of mass ratio $f(q)$ and orbital period $f(P)$ of early-type stars (comprised of O-, B-, and A-type stars) and investigate their dependences on effective temperature $T_{\rm eff}$, stellar metallicity [M/H], and the projection velocity $v\sin{i}$, based on the homogeneous spectroscopic sample from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release Eight (DR8). We found that $f_{\rm b}^{\rm in}$ increases with increasing $T_\mathrm{eff}$. The binary fraction is positively correlated with metallicity for spectra in the sample. Over all the $v\sin{i}$ values we considered, the $f_{\rm b}^{\rm in}$ have constant values of $\sim$50\%. It seems that the binary population is relatively evenly distributed over a wide range of $v\sin{i}$ values, while the whole sample shows that most of the stars are concentrated at low values of $v\sin{i}$ (probably from strong wind and magnetic braking of single massive stars) and at high values of $v\sin{i}$ (likely from the merging of binary stars). Stellar evolution and binary interaction may be partly responsible for this.There are no correlations found between $\pi$($\gamma$) and $T_{\rm eff}$, nor for $\pi$($\gamma$) and [M/H]. The uncertainties of the distribution decrease toward a larger sample size with higher observational cadence.