Related papers: Target Selection for the SDSS-IV APOGEE-2 Survey
The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared…
The Sloan Digital Sky Survey--III (SDSS--III) Apache Point Observatory Galactic Evolution Experiment (APOGEE) has obtained high resolution (R $\sim$ 22,500), high signal-to-noise ratio ($>$ 100) spectra in the H$-$band ($\sim$1.5$-$1.7…
Although originally conceived as primarily an extragalactic survey, the Sloan Digital Sky Survey (SDSS-I), and its extensions SDSS-II and SDSS-III, continue to have a major impact on our understanding of the formation and evolution of our…
Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to…
The Apache Point Observatory Galactic Evolution Experiment (APOGEE) - one of the Sloan Digital Sky Survey III programs -- is using near-infrared spectra of $\sim 100,000$ red giant branch star candidates to study the structure of the Milky…
We describe the algorithm used to select the Luminous Red Galaxy (LRG) sample for the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey IV (SDSS-IV) using photometric data from both the SDSS and the…
We train a convolutional neural network, APOGEE Net, to predict $T_\mathrm{eff}$, $\log g$, and, for some stars, [Fe/H], based on the APOGEE spectra. This is the first pipeline adapted for these data that is capable of estimating these…
We describe the target selection and resulting properties of a spectroscopic sample of luminous, red galaxies (LRG) from the imaging data of the Sloan Digital Sky Survey (SDSS). These galaxies are selected on the basis of color and…
The Sloan Digital Sky Survey-II Supernova Survey has identified a large number of new transient sources in a 300 sq. deg. region along the celestial equator during its first two seasons of a three-season campaign. Multi-band (ugriz) light…
The Sloan Digital Sky Survey (SDSS) is making a multi-colour, three dimensional map of the nearby Universe. The survey is in two parts. The first part is imaging one quarter of the sky in five colours from the near ultraviolet to the near…
This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point…
In its three years of operation, the Sloan Digital Sky Survey (SDSS-III) Apache Point Observatory Galactic Evolution Experiment (APOGEE-1) observed $>$14,000 stars with enough epochs over a sufficient temporal baseline for the fitting of…
This paper describes the Fifth Data Release (DR5) of the Sloan Digital Sky Survey (SDSS). DR5 includes all survey quality data taken through June 2005 and represents the completion of the SDSS-I project (whose successor, SDSS-II will…
We present a semi-empirical spectral classification scheme for normal B-type stars using near-infrared spectra (1.5-1.7 $\mu$m) from the SDSS APOGEE2-N DR14 database. The main motivation for working with B-type stars is their importance in…
Galactic interstellar extinction maps are powerful and necessary tools for Milky Way structure and stellar population analyses, particularly toward the heavily-reddened bulge and in the midplane. However, due to the difficulty of obtaining…
The Sloan Digital Sky Survey has recently initiated its 5th survey generation (SDSS-V), with a central focus on stellar spectroscopy. In particular, SDSS-V Milky Way Mapper program will deliver multi-epoch optical and near-infrared spectra…
The goal of this paper is to describe the science verification of Milky Way Mapper (MWM) APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP) data products published in Data Release 19 (DR19) of the fifth phase of the Sloan…
Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…
The data volume generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and…
We explore the synergy between photometric and spectroscopic surveys by searching for periodic variable stars among the targets observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) using photometry from the…