Related papers: A Machine Learning Method to Infer Fundamental Ste…

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,…

The first step toward doing high-precision astrometry is the measurement of individual stars in individual images, a step that is fraught with dangers when the images are undersampled. The key to avoiding systematic positional error in…

Context. Being the most numerous and oldest stars in the galaxy, M dwarfs are objects of great interest for exoplanet searches. The presence of molecules in their atmosphere complicates our understanding of their atmospheric properties. But…

(Abbreviated) Kepler planet candidates require both spectroscopic and imaging follow-up observations to rule out false positives and detect blended stars. [...] In this paper, we examine a sample of 11 Kepler host stars with companions…

We present equivalent widths, improved model atmosphere parameters, and revised abundances for 14 species of 11 elements derived from high resolution optical spectroscopy of 311 metal-poor stars. All of these stars had their parameters…

Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem.…

SDSS-V will obtain 100,000s of medium-resolution, optical spectra of M dwarfs with the BOSS instrument. M dwarfs have complex atmospheres, and their spectra contain many wide and dense, overlapping molecular features, so determining…

The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar…

We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21,…

NASA's Kepler, K2 and TESS missions employ Simple Aperture Photometry (SAP) to derive time-series photometry, where an aperture is estimated for each star, and pixels containing each star are summed to create a single light curve. This…

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low Resolution Spectroscopic Survey (LRS) provides massive spectroscopic data of M-type stars, and the derived stellar parameters could bring vital help to various…

Getting spectra at good signal-to-noise ratios takes orders of magnitudes more time than photometric observations. Building on the technique developed for photometric redshift estimation of galaxies, we develop and demonstrate a…

We use empirical star formation histories (SFHs), measured from HST-based resolved star color-magnitude diagrams, as input into population synthesis codes to model the broadband spectral energy distributions (SEDs) of ~50 nearby dwarf…

The bibliographical compilation of stellar atmospheric parameters (Teff, logg, [Fe/H]) relying on high-resolution, high signal-to-noise spectroscopy started in the eighties with the so-called [Fe/H] catalogue, and was continued in 2010 with…

In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T_eff), surface gravity (log…

Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin…

The development of the state-of-the-art telescopic systems capable of performing expansive sky surveys such as the Sloan Digital Sky Survey, Euclid, and the Rubin Observatory's Legacy Survey of Space and Time (LSST) has significantly…

Aligning physiological parameter labels with large-scale photoplethysmographic (PPG) data for deep learning is challenging and resource-intensive. While self-supervised representation learning (SSRL) can handle limited annotated data, the…

The SEGUE survey obtained 240,000 moderate resolution (R = 1800) spectra from 3900 - 9000 Angstroms of fainter Milky Way stars (14.0 < g < 20.3) of a wide variety of spectral types, both main sequence and evolved objects, with the goal of…

We describe the photometric calibration and stellar classification methods used to produce the Kepler Input Catalog (KIC). The KIC is a catalog containing photometric and physical data for sources in the Kepler Mission field of view; it is…