Table · dataset · 2018
HI gas mass fraction estimations
Listed in IVOA Registry (Virtual Observatory)
The application of artificial neural networks (ANNs) for the estimation of HI gas mass fraction (M_HI_/M*) is investigated, based on a sample of 13 674 galaxies in the Sloan Digital Sky Survey (SDSS) with HI detections or upper limits from the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA).
Description
We show that, for an example set of fixed input parameters (g-r colour and i-band surface brightness), a multidimensional quadratic model yields M_HI_/M* scaling relations with a smaller scatter (0.22dex) than traditional linear fits (0.32dex), demonstrating that non-linear methods can lead to an improved performance over traditional approaches.
A more extensive ANN analysis is performed using 15 galaxy parameters that capture variation in stellar mass, internal structure, environment and star formation. Of the 15 parameters investigated, we find that g-r colour, followed by stellar mass surface density, bulge fraction and specific star formation rate have the best connection with M_HI_/M*. By combining two control parameters, that indicate how well a given galaxy in SDSS is represented by the ALFALFA training set (PR) and the scatter in the training procedure ({sigma}_fit_), we develop a strategy for quantifying which SDSS galaxies our ANN can be adequately applied to, and the associated errors in the M_HI_/M* estimation.
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In contrast to previous works, our M_HI_/M* estimation has no systematic trend with galactic parameters such as M*, g-r and star formation rate. We present a catalogue of M_HI_/M* estimates for more than half a million galaxies in the SDSS, of which ~150000 galaxies have a secure selection parameter with average scatter in the M_HI_/M* estimation of 0.22dex.
Links
Where it is published
- Reference page cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/464/3796 ↗
landing page · from IVOA Registry
Documentation and papers
- ADS 2017MNRAS.464.3796T ui.adsabs.harvard.edu/abs/2017MNRAS.464.3796T ↗
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/mnras/464/3796 ↗
catalogue entry · from IVOA Registry
Topics
- Stated by source
- Radio
- From keywords
- Astronomy & Astrophysics
- Inferred from text
- Astronomical sciences 70%
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| IVOA Registry (Virtual Observatory) | ivo://cds.vizier/j/mnras/464/3796 | 15 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 (70%) |
| concepts[field].local:field:astronomy | mapping · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| concepts[modality].ivoa_waveband:radio | source · IVOA Registry | connector:ivoa_registry@1.0.0 | |
| 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 |