Table · dataset · 2015
Abundances from SEGUE Stellar Parameters Pipeline
Listed in IVOA Registry (Virtual Observatory)
A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >10^9^ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~fewx10^6^ targets.
Description
As we approach the Large Synoptic Survey Telescope era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (T_eff_, logg, and [Fe/H]) using photometric-brightness variations and color alone.
A training set is constructed from a systematic spectroscopic survey of variables with Hectospec/Multi-Mirror Telescope. In sum, the training set includes ~9000 spectra, for which stellar parameters are measured using the SEGUE Stellar Parameters Pipeline (SSPP). We employed the random forest algorithm to perform a non-parametric regression that predicts T_eff_, logg, and [Fe/H] from photometric time-domain observations.
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Our final optimized model produces a cross-validated rms error (RMSE) of 165K, 0.39dex, and 0.33dex for T_eff_, logg, and [Fe/H], respectively. Examining the subset of sources for which the SSPP measurements are most reliable, the RMSE reduces to 125K, 0.37dex, and 0.27dex, respectively, comparable to what is achievable via low-resolution spectroscopy. For variable stars this represents a {approx}12%-20% improvement in RMSE relative to models trained with single-epoch photometric colors.
As an application of our method, we estimate stellar parameters for ~54000 known variables. We argue that this method may convert photometric time-domain surveys into pseudo-spectrographic engines, enabling the construction of extremely detailed maps of the Milky Way, its structure, and history.
Links
Where it is published
- Reference page cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/798/122 ↗
landing page · from IVOA Registry
Documentation and papers
- ADS 2015ApJ...798..122M ui.adsabs.harvard.edu/abs/2015ApJ...798..122M ↗
publication · from IVOA Registry
Catalogue records · 2
- VOResource record dc.g-vo.org/oai.xml?verb=GetRecord&metadataPrefix=ivo_vor&identifier=ivo%3… ↗
metadata API · from IVOA Registry
- Registry record (GAVO) dc.g-vo.org/I/cds.vizier/j/apj/798/122 ↗
catalogue entry · from IVOA Registry
Topics
- Stated by source
- Optical
- From keywords
- Astronomy & Astrophysics
- Inferred from text
- Astronomical sciences 74% · Imaging 75%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| IVOA Registry (Virtual Observatory) | ivo://cds.vizier/j/apj/798/122 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| concepts[field].anzsrc:group:5101 | enrichment · IVOA Registry | taxonomy-embedding@1.1.0 | title+keywords+description (74%) |
| concepts[field].local:field:astronomy | mapping · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| concepts[modality].ivoa_waveband:optical | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| concepts[modality].local:modality:imaging | enrichment · IVOA Registry | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · IVOA Registry | connector:ivoa_registry@1.0.0 | rr.resource.res_description |
| license_text | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| publication_date | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| title | source · IVOA Registry | connector:ivoa_registry@1.0.0 | rr.resource.res_title |
| updated_date | source · IVOA Registry | connector:ivoa_registry@1.0.0 |