Mapping the Milky Way with Gaia Bp/Rp spectra I: Systematic flux corrections and atmospheric parameters for 68 million stars Article Swipe
Xianhao Ye
,
Wenbo Wu
,
Carlos Allende Prieto
,
David S. Aguado
,
Jingkun Zhao
,
J. I. Gónzalez Hernández
,
R. Rébolo
,
Gang Zhao
,
Zhuohan Li
,
C. del Burgo
,
Yuqin Chen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.19105
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.19105
Gaia Bp/Rp spectra for over two hundred million stars have great potential for mapping metallicity across the Milky Way. We aim to construct an alternative catalog of atmospheric parameters from Gaia Bp/Rp spectra by fitting them with synthetic spectra based on model atmospheres, and provide corrections to the Bp/Rp fluxes according to stellar colors, magnitudes, and extinction. We use GaiaXPy to obtain calibrated spectra and apply FERRE to match the corrected Bp/Rp spectra with models and infer atmospheric parameters. We train a neural network using stars in APOGEE to predict flux corrections as a function of wavelength for each target. Based on the comparison with APOGEE parameters, we conclude that our estimated parameters have systematic errors and uncertainties in $T_{\mathrm{eff}}$, $\log g$, and [M/H] about $-38 \pm 167$ K, $0.05 \pm 0.40$ dex, and $-0.12 \pm 0.19$ dex, respectively, for stars in the range $4000 \le T_{\mathrm{eff}} \le 7000$ K. The corrected Bp/Rp spectra show better agreement with both models and Hubble Space Telescope CALSPEC data. Our correction increases the precision of the relative spectrophotometry of the Bp/Rp data from $3.2\% - 3.7\%$ to $1.2\% - 2.4\%$. Finally, we have built a catalog of atmospheric parameters for stars within $4000 \le T_{\mathrm{eff}} \le 7000$ K, comprising $68,394,431$ sources, along with a subset of $124,188$ stars with $\mathrm{[M/H]} \le -2.5$. Our results confirm that the Gaia Bp/Rp flux calibrated spectra show systematic patterns as a function of wavelength that are tightly related to colors, magnitudes, and extinction. Our optimization algorithm can give us accurate atmospheric parameters of stars with a clear and direct link to models of stellar atmospheres, and can be used to efficiently search for extremely metal-poor stars.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.19105
- https://arxiv.org/pdf/2411.19105
- OA Status
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405030059
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405030059Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.19105Digital Object Identifier
- Title
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Mapping the Milky Way with Gaia Bp/Rp spectra I: Systematic flux corrections and atmospheric parameters for 68 million starsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-28Full publication date if available
- Authors
-
Xianhao Ye, Wenbo Wu, Carlos Allende Prieto, David S. Aguado, Jingkun Zhao, J. I. Gónzalez Hernández, R. Rébolo, Gang Zhao, Zhuohan Li, C. del Burgo, Yuqin ChenList of authors in order
- Landing page
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https://arxiv.org/abs/2411.19105Publisher landing page
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https://arxiv.org/pdf/2411.19105Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.19105Direct OA link when available
- Concepts
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Milky Way, Stars, Physics, Flux (metallurgy), Astrophysics, Spectral line, Systematic error, Astronomy, Materials science, Mathematics, Metallurgy, StatisticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.apply | 65 |
| abstract_inverted_index.based | 39 |
| abstract_inverted_index.built | 190 |
| abstract_inverted_index.clear | 259 |
| abstract_inverted_index.data. | 165 |
| abstract_inverted_index.great | 10 |
| abstract_inverted_index.infer | 76 |
| abstract_inverted_index.match | 68 |
| abstract_inverted_index.model | 41 |
| abstract_inverted_index.range | 143 |
| abstract_inverted_index.stars | 8, 85, 140, 197, 214, 256 |
| abstract_inverted_index.train | 80 |
| abstract_inverted_index.using | 84 |
| abstract_inverted_index.$-0.12 | 134 |
| abstract_inverted_index.$1.2\% | 184 |
| abstract_inverted_index.$3.2\% | 180 |
| abstract_inverted_index.-2.5$. | 218 |
| abstract_inverted_index.3.7\%$ | 182 |
| abstract_inverted_index.APOGEE | 87, 105 |
| abstract_inverted_index.Hubble | 161 |
| abstract_inverted_index.across | 15 |
| abstract_inverted_index.better | 155 |
| abstract_inverted_index.direct | 261 |
| abstract_inverted_index.errors | 115 |
| abstract_inverted_index.fluxes | 49 |
| abstract_inverted_index.models | 74, 159, 264 |
| abstract_inverted_index.neural | 82 |
| abstract_inverted_index.obtain | 61 |
| abstract_inverted_index.search | 274 |
| abstract_inverted_index.stars. | 278 |
| abstract_inverted_index.subset | 211 |
| abstract_inverted_index.within | 198 |
| abstract_inverted_index.2.4\%$. | 186 |
| abstract_inverted_index.CALSPEC | 164 |
| abstract_inverted_index.GaiaXPy | 59 |
| abstract_inverted_index.catalog | 25, 192 |
| abstract_inverted_index.colors, | 53, 242 |
| abstract_inverted_index.confirm | 221 |
| abstract_inverted_index.fitting | 34 |
| abstract_inverted_index.hundred | 6 |
| abstract_inverted_index.mapping | 13 |
| abstract_inverted_index.million | 7 |
| abstract_inverted_index.network | 83 |
| abstract_inverted_index.predict | 89 |
| abstract_inverted_index.provide | 44 |
| abstract_inverted_index.related | 240 |
| abstract_inverted_index.results | 220 |
| abstract_inverted_index.spectra | 2, 32, 38, 63, 72, 153, 228 |
| abstract_inverted_index.stellar | 52, 266 |
| abstract_inverted_index.target. | 99 |
| abstract_inverted_index.tightly | 239 |
| abstract_inverted_index.Finally, | 187 |
| abstract_inverted_index.accurate | 252 |
| abstract_inverted_index.conclude | 108 |
| abstract_inverted_index.function | 94, 234 |
| abstract_inverted_index.patterns | 231 |
| abstract_inverted_index.relative | 173 |
| abstract_inverted_index.sources, | 207 |
| abstract_inverted_index.$124,188$ | 213 |
| abstract_inverted_index.Telescope | 163 |
| abstract_inverted_index.according | 50 |
| abstract_inverted_index.agreement | 156 |
| abstract_inverted_index.algorithm | 248 |
| abstract_inverted_index.construct | 22 |
| abstract_inverted_index.corrected | 70, 151 |
| abstract_inverted_index.estimated | 111 |
| abstract_inverted_index.extremely | 276 |
| abstract_inverted_index.increases | 168 |
| abstract_inverted_index.potential | 11 |
| abstract_inverted_index.precision | 170 |
| abstract_inverted_index.synthetic | 37 |
| abstract_inverted_index.calibrated | 62, 227 |
| abstract_inverted_index.comparison | 103 |
| abstract_inverted_index.comprising | 205 |
| abstract_inverted_index.correction | 167 |
| abstract_inverted_index.metal-poor | 277 |
| abstract_inverted_index.parameters | 28, 112, 195, 254 |
| abstract_inverted_index.systematic | 114, 230 |
| abstract_inverted_index.wavelength | 96, 236 |
| abstract_inverted_index.alternative | 24 |
| abstract_inverted_index.atmospheric | 27, 77, 194, 253 |
| abstract_inverted_index.corrections | 45, 91 |
| abstract_inverted_index.efficiently | 273 |
| abstract_inverted_index.extinction. | 56, 245 |
| abstract_inverted_index.magnitudes, | 54, 243 |
| abstract_inverted_index.metallicity | 14 |
| abstract_inverted_index.parameters, | 106 |
| abstract_inverted_index.parameters. | 78 |
| abstract_inverted_index.$68,394,431$ | 206 |
| abstract_inverted_index.atmospheres, | 42, 267 |
| abstract_inverted_index.optimization | 247 |
| abstract_inverted_index.respectively, | 138 |
| abstract_inverted_index.uncertainties | 117 |
| abstract_inverted_index.$\mathrm{[M/H]} | 216 |
| abstract_inverted_index.T_{\mathrm{eff}} | 146, 201 |
| abstract_inverted_index.spectrophotometry | 174 |
| abstract_inverted_index.$T_{\mathrm{eff}}$, | 119 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.20042048 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |