MaNGA Stellar Library (MaStar)
As one component of SDSS-IV, we built a stellar spectral library with a very comprehensive stellar parameter coverage, a large sample size, and high quality calibrations, using the same instrument as used by the MaNGA survey. An overview of the program and the first release of MaStar data are described by Yan et al. (2019).
The fiber plugplate system in SDSS-IV enables parallel spectroscopic observations between infrared and optical. At the same time when the APOGEE-2 survey is observing with their infrared spectrograph during bright time, we can take optical spectra of other stars in the same field of view with the MaNGA fiber bundles. The large amount of observing time and the wide sky coverage of the APOGEE-2 survey enabled us to build a large, comprehensive, and homogeneous stellar spectral library in the optical.
The final set of the MaStar stellar library spectra are now publicly available in DR17.
- Bright-time parallel observations
- Fall 2014 - Summer 2020
- 17 science targets and 12 calibration standards per 7 deg2 plate
- Wavelength: 362-1035 nm, resolution R~1800
- Nearly 12,000 unique library stars in wide areas of the sky
- More than 12,000 unique calibration standards
- Typically 2-3 epochs with a total of 1 hour exposure per epoch
- Magnitude range between 8 and 17.5 in g-band or i-band
- S/N per pixel (1.1-1.4 Angstrom) per epoch ranges from 50 to 230 (16th-84th percentiles).
A stellar library needs to be comprehensive in terms of coverage of different stellar types and stellar parameters. We strive to cover as wide a range as possible in each of the four stellar parameters: effective temperature (Teff), surface gravity (log g), metallicity ([Fe/H]), and the alpha-elements-to-iron ratio ([α/Fe]). We achieve this by targeting stars from existing stellar parameter catalogs from a variety of spectroscopic surveys, including APOGEE-1, APOGEE-2, SDSS/SEGUE, and LAMOST. We also estimated stellar parameters for millions of stars using PanSTARRS1 and APASS photometry and we use these to supplement extreme regions of stellar parameter space.
After Gaia Data Release 2, we also made use of Gaia information to select hot main sequence stars, blue giants, blue supergiants, red supergiants, very cool dwarfs, metal-poor cool dwarfs, extreme horizontal branch stars, and white dwarfs. These selection strategies provide us a comprehensive stellar parameter coverage for our library.
- Renbin Yan (Chinese University of Hong Kong) (PI)
- Yanping Chen (NYU Abu Dhabi)
- Daniel Lazarz
(University of Kentucky)
- Claudia Maraston
(University of Portsmouth)
- Dmitry Bizyaev (NMSU, APO)
- Lewis Hill (University of Portsmouth)
- Julie Imig (NMSU)
- Daniel Thomas
University of Portsmouth)
- Jon Holtzman (NMSU)
- Sofia Meneses-Goytia (University of Surrey)
- Guy Stringfellow
(University of Colorado, Boulder)
- David R. Law (STScI)
- Jesus Falcon Barroso (IAC)
- Zheng Zheng (NAOC)
- Eddie Schlafly (LBNL)
- Ronald Wilhelm
(University of Kentucky)
- Kyle McCarthy (Industry)
- MaNGA and APOGEE operation teams
What's New About MaStar?
Stellar spectral libraries need to have high quality calibrations, including flux calibration, wavelength calibration, and line spread function (LSF) calibration. Although millions of stars have been observed by previous generations of SDSS and by LAMOST, those spectra are taken with single fibers which do not capture all the flux. Due to alignment errors and atmospheric effects, it is very difficult, if not impossible, for single-fiber spectroscopy to achieve a relative flux calibration accuracy to better than 10% for individual stellar spectra. Using fiber bundles, we can achieve much more accurate flux calibration, as the multiple fibers in a bundle allow us to accurately determine the fraction of light covered by each fiber as a function of wavelength. In addition, we will obtain spectra with much higher signal-to-noise ratio and wider wavelength coverage than SDSS/SEGUE and LAMOST.
MaStar also covers a much wider range of stellar parameters than previous spectroscopic survey programs.