Related papers: Deep learning interpretability analysis for carbon…

This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted…

We present {\tt deepSIP} (deep learning of Supernova Ia Parameters), a software package for measuring the phase and -- for the first time using deep learning -- the light-curve shape of a Type Ia supernova (SN~Ia) from an optical spectrum.…

This paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second $Gaia$ data release, based on the combination of the stars in four spectroscopic surveys:…

Astrometry from Gaia has enabled discovery of three dormant black holes (BHs) in au-scale binaries. Numerous models have been proposed to explain their formation, including several that have forecasted Gaia detections. However, previous…

The extremely accurate estimates of stellar variability and radial velocity in the Gaia Data Release 3 (Gaia DR3) have enabled us to examine the close binarity and radial velocity (RV) of central stars (CSs) of planetary nebulae (PNe). This…

Despite the advances provided by large-scale photometric surveys, stellar features - such as metallicity - generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a…

In this work, we use the spectroscopy-based stellar color regression (SCR) method with ~ 0.7 million common stars between LAMOST DR7 and Gaia EDR3 to acquire color corrections in G - GRP and GBP - GRP. A sub-mmag precision is achieved. Our…

In the current era, in which an unprecedented wealth of data is available for the study of the Milky Way, the Gaia Benchmark Stars (GBS) have become an established reference and calibration sample. Studies of stellar structure and…

We present results from the analysis of 88 carbon stars selected from Hamburg/ESO (HES) survey using low-resolution spectra (R$\sim$1330 \& 2190). The spectra were obtained with the Himalayan Faint Object Spectrograph Camera (HFOSC)…

Different stellar populations may be identified through differences in chemical, kinematic, and chronological properties, suggesting the interplay of various physical mechanisms that led to their origin and subsequent evolution. As such,…

We present the first Gaia catalogue of eclipsing binary candidates released in Gaia DR3, describe its content, provide tips for its usage, estimate its quality, and show illustrative samples. The catalogue contains 2,184,477 sources with G…

Gaia is an astrometric space experiment that is measuring positions, proper motions as well as parallaxes for a huge number of stars. It operates a medium-dispersion spectrometer, the RVS, that provides spectra and thus radial velocity…

We provide a catalogue of atmospheric parameters for 1,806,921 cool dwarfs from Gaia DR3 which lie within the range covered by LAMOST cool dwarf spectroscopic parameters: 3200 K < T_{eff}< 4300 K, -0.8 < [M/H] < 0.2 dex, and 4.5 <log{g} <…

The latest Gaia data release provides low-resolution spectra for approximately 100 000 white dwarfs. Though useful for pre-classification, they lack the resolution required for accurate spectral type and parameter determination, motivating…

The accurate determination of chemical abundances in stars plays a pivotal role in understanding stellar structure and evolution, nucleosynthesis, and the chemical enrichment history of the Milky Way. Benchmark stars with precise and…

Benefiting from the GAIA second and early third releases of photometric and astrometric data we examine the population of asymptotic giant branch (AGB) stars that appear in the fields of intermediate-age and young open star clusters. We…

Context. Gaia DR3 has offered the scientific community a remarkable dataset of approximately one million spectra acquired with the Radial Velocity Spectrometer (RVS) in the Calcium II triplet region, that is well-suited to identify very…

Context. In the current ever increasing data volumes of astronomical surveys, automated methods are essential. Objects of known classes from the literature are necessary for training supervised machine learning algorithms, as well as for…

Early and accurate detection through Pap smear analysis is critical to improving patient outcomes and reducing mortality of Cervical cancer. State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) require substantial computational…

We present a new grid of stellar model calculations for stars on the Asymptotic Giant Branch between 1.0 and 6.0 M_sun. Our grid consists of 5 chemical mixtures between Z=0.0005 and Z=0.04, with both solar-like and $\alpha$-element enhanced…