Table · dataset · 2024
GES Catalogue HR10 & HR21 with cINN
Listed in IVOA Registry (Virtual Observatory)
New spectroscopic surveys will increase the number of astronomical objects in need of characterisation by more than an order of magnitude.
Description
Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine learning algorithms cannot directly estimate error, making them unsuitable for reliable science.We aim to train a supervised deep-learning algorithm tailored for high-resolution observational stellar spectra.
This algorithm accurately infers precise estimates while providing coherent estimates of uncertainties by leveraging information from both the neural network and the spectra.We trained a conditional invertible neural network (cINN) on observational spectroscopic data obtained from the GIRAFFE spectrograph (HR10 and HR21 setups) within the Gaia-ESO survey. A key feature of cINN is its ability to produce the Bayesian posterior distribution of parameters for each spectrum.
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By analysing this distribution, we inferred stellar parameters and their corresponding uncertainties. We carried out several tests to investigate how parameters are inferred and errors are estimated.We achieved an accuracy of 28K in Teff, 0.06dex in logg, 0.03dex in [Fe/H], and between 0.05dex and 0.17dex for the other abundances for high-quality spectra. Accuracy remains stable with low signal-to-noise ratio (between 5 and 25) spectra, with an accuracy of 39K in Teff, 0.08dex in logg, and 0.05dex in [Fe/H].
The uncertainties obtained are well within the same order of magnitude. The network accurately reproduces astrophysical relationships both on the scale of the Milky Way and within smaller star clusters. We created a table containing the new parameters generated by our cINN.This neural network represents a compelling proposition for future astronomical surveys.
These derived uncertainties are coherent and can therefore be reused in future works as Bayesian priors.
Links
Where it is published
- Reference page cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/692/A228 ↗
landing page · from IVOA Registry
Documentation and papers
- ADS 2024A&A...692A.228C ui.adsabs.harvard.edu/abs/2024A%26A...692A.228C ↗
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/a%2Ba/692/a228 ↗
catalogue entry · from IVOA Registry
Topics
- Stated by source
- Optical
- From keywords
- Astronomy & Astrophysics
- Inferred from text
- Astronomical sciences 72% · Tabular 65%
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| IVOA Registry (Virtual Observatory) | ivo://cds.vizier/j/a+a/692/a228 | 11 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 (72%) |
| 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:tabular | enrichment · IVOA Registry | keyword-concept-rules@1.0.0 | title+description (65%) |
| 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 |