1. Can you describe your role in the SDSS collaboration and what you enjoy most about it? I am the former Lead Scientist for SDSS-V Chile (2021-2023) and CoCo representative of the Chilean National Time Allocation Committee (CNTAC, 2020-2021). I enjoy the collaborative spirit in different areas of SDSS-V and the respect among their members […]
Search Results for: SDSS-IV
Image Gallery
This page shows images associated with the Sloan Digital Sky Survey. For more images, see our previous image galleries. To reuse these images, see Image Permissions. SDSS and du Pont Telescopes Focal Plane System Local Volume Mapper Milky Way Mapper Other SDSS Image Galleries Many more images can be found on the Image Galleries of […]
Zora and Valis
Zora and Valis are new user interfaces for accessing and exploring new data in SDSS-V. They complement the legacy Science Archive Webapp (SAW) for accessing SDSS-IV optical and infrared spectra. They provide ways of searching for SDSS data, exploring observed target metadata, and visualizing or accessing spectral data. Zora is a modern, reactive, component-based User […]
Output Files
Astra Data Products Data products that are produced by Astra can be categorized in the following ways: We do not coadd spectra across instruments that were observed at from both observatories (e.g., we do not co-add APOGEE spectra from LCO with APOGEE spectra from APO). The pipeline spectrum data products all share a reasonably consistent […]
The Payne
Summary The Payne (Ting et al. 2019) uses a two-hidden-layer neural network to determine 25 stellar labels simultaneously given a rest-frame resampled APOGEE spectrum. The network is trained on a set of synthetic spectra based on Kurucz atmospheric models. For DR19, we use the same synthetic spectra and model as in Ting et al. (2019). […]
AstroNN
AstroNN Summary AstroNN (Leung & Bovy 2019) uses a deep-learning artificial neural network to determine stellar parameters (Teff, log g and metallicity) and 18 individual element abundances with associated uncertainty given a rest-frame resampled APOGEE spectrum. The network is trained on high quality APOGEE spectra and labels from SDSS-IV Data Release 17. Detailed Description AstroNN […]
ASPCAP
Summary APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP) is a pipeline for measuring stellar parameters and chemical abundances from APOGEE spectra. ASPCAP wraps the FERRE code (Allende Prieto et al. 2006), which interpolates spectra from a (potentially high dimensional) rectilinear grid and minimizes the χ2 statistic. FERRE includes options to simultaneously fit the continuum, […]
ApogeeNet
Summary ApogeeNet (Olney et al. 2020; Sprague et al. 2022; Sizemore et al. 2024 ) uses a convolutional neural network to estimate stellar labels (effective temperature, surface gravity, and metallicity) given a rest-frame resampled APOGEE spectrum. The network is trained on high quality APOGEE spectra and labels from SDSS-IV Data Release 17. Detailed Description ApogeeNet […]
Moving from APOGEE DR17 to DR19
Many science cases, such as chemical cartography of the Galaxy, will want to use the DR19 dataset to expand upon DR17 results, employing similar analysis. In this page, we highlight some key differences between the versions of ASPCAP in DR17 and DR19 and between the structure of the files that hold the ASPCAP data. We […]
Astra
Astra is the glue that brings together analysis methods into a single software framework. MWM observes stars across the HR diagram with both the BOSS and APOGEE spectrographs. This wide range of stellar parameters, resolution, and wavelength coverage means that a single pipeline is not currently suitable. Links on the sidebar will take you to […]