A catalog of the Galactic population of X-ray pulsars in high-mass X-ray binary (HMXB) systems is presented. It contains information about 82 confirmed sources: 18 persistent and 64 transient pulsars. Their basic parameters include spin period, spin evolution with global and local spin-up/spin-down and duration, orbital period, X-ray luminosity, magnetic field strength measured by cyclotron line analysis, distance, spectral and luminosity class, and observable parameters of massive companions, which are shown in the tables provided, with corresponding references. Candidates of HMXB pulsars are also listed for further careful consideration.

Digitized archival astronomical data serve as a valuable resource for studying variable stars and their evolution. This study analyzes variable stars based on archival photometric data obtained with the Schmidt camera at the Fesenkov Astrophysical Institute from 1960 to 1989.Cross-identification with the General Catalog of Variable Stars catalog revealed 20,961 variable stars in the archival frames. Additionally, matching with Gaia DR3 and TESS expanded the characterization of these stars, including galactic height, variability amplitude, spatial distribution, and identification of objects with archival observations. These parameters had not been previously compiled in a single catalog.To further enrich the data, machine learning methods were applied, allowing the classification of objects with incomplete characteristic data and the prediction of their spectral types and variability classes. The analysis demonstrated that XGBoost achieved the highest accuracy among the tested models (78.9% for variability types and 86.46% for spectral types) and identified the most significant parameters influencing stellar variability.The final catalog has been published on the Kazakhstani National Virtual Observatory platform and is available for further research.

We present empirical effective temperature ( T _eff ) calibrations for Galactic supergiants of spectral types B5–A5 based on optical spectra. The relationships were derived from a reference sample with adopted literature temperatures and use equivalent widths, central line depths, and ratios of selected spectral features as temperature indicators. Quadratic relationships are established for individual diagnostics and their ratios. Collectively, the calibrations span a T _eff interval from 8400 to 14,700 K, while the validity range of each relationship is individually specified. Detailed quantitative atmospheric analyses remain indispensable for deriving physically consistent stellar parameters. However, their application to extensive spectroscopic samples is observationally and computationally demanding. The empirical relationships presented here provide a homogeneous and readily applicable temperature scale for the characterization of Galactic BA supergiants. The complete calibration tables, including fitted coefficients and uncertainty information, together with an interactive Python tool to apply the relationships, are publicly available through Zenodo. The reduced continuum-normalized spectra and associated metadata are published through a Virtual Observatory–compliant service of the Kazakhstan National Virtual Observatory.

Digitized archival astronomical observations provide a valuable basis for the study of variable stars and their long-term behavior. This work presents an analysis of variable stars based on archival photometric data obtained with a Schmidt camera at the Fesenkov Astrophysical Institute during the period from 1960 to 1989. The detected objects were cross-matched with existing catalogs of variable stars and modern astrometric and photometric databases, allowing the compilation of an extended set of stellar parameters that had not previously been combined within a single dataset. To complement incomplete catalog information, machine learning methods were applied to classify stellar variability types and spectral classes using available astrometric and photometric features. Several classification models were tested, and the most stable and accurate approach was selected for further analysis. The resulting catalog integrates archival observations, catalog cross-identification results, and predicted stellar characteristics, providing an extended resource for studies of stellar variability and long-term photometric evolution.

This paper presents an automated pipeline for wavelength calibration of slit-based astronomical spectra, combining robust peak-matching techniques with a lightweight preprocessing step. The method is based on initial peak alignment using Dynamic Time Warping (DTW), followed by refinement of the dispersion solution with the Random Sample Consensus (RANSAC) algorithm. To ensure consistency between observed and reference spectra, a compact one-dimensional convolutional neural network (CNN) is employed at the preprocessing stage to identify the spectral range and corresponding diffraction grating. This step enables the selection of an appropriate reference spectrum but does not directly affect the calibration procedure. The DTW-based approach provides reliable initial correspondences between spectral peaks under nonlinear distortions and noise, while RANSAC filtering removes incorrect matches and yields a robust polynomial dispersion solution. The method is validated on real observational data from calibration lamps and demonstrates stable performance, high matching accuracy, and full automation capability. Compared to traditional manual or semi-automatic approaches, the proposed pipeline significantly reduces processing time while maintaining calibration precision. The developed solution is planned to be deployed as a service in the virtual observatory infrastructure and is applicable for processing numerous spectral data.