Academic Journal
An adaptive parameter estimator for poor-quality spectral data of white dwarfs.
| Τίτλος: | An adaptive parameter estimator for poor-quality spectral data of white dwarfs. |
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| Συγγραφείς: | Zhang, Jiangchuan, Xie, Duo, Bu, Yude, Yi, Zhenping, Liu, Meng, Kong, Xiaoming |
| Πηγή: | Astronomy & Astrophysics / Astronomie et Astrophysique; 2025, Vol. 704, p1-13, 13p |
| Θεματικοί όροι: | White dwarf stars, Parameter estimation, Spectrum analysis instruments, Machine learning, Data quality, Adaptive estimation (Statistics), Algorithms, Stellar evolution |
| Περίληψη: | White dwarfs represent the end stage for 97% of stars, making precise parameter measurement crucial for understanding stellar evolution. Traditional estimation methods involve fitting spectra or photometry, which require high-quality data. In recent years, machine learning has played a crucial role in processing spectral data due to its speed, automation, and accuracy. However, two common issues have been identified. First, most studies rely on data with high signal-to-noise ratios (S/N > 10), leaving many poor-quality datasets underutilized. Second, existing machine learning models, primarily based on convolutional and recurrent networks and their variants, fail to simultaneously capture both local spatial and long-range sequential spectral information. To address these challenges, we designed the estimator network (EstNet), an advanced algorithm integrating multiple techniques, including residual networks, squeeze and excitation attention, gated recurrent units, adaptive loss, and Monte Carlo dropout. A total of 5965 poor-quality white dwarf spectra (R~1800, S/N~1.17) were used for the experiments. EstNet achieved average percentage errors of 13.81% for effective temperature and 3.92% for surface gravity on the test set. These results are superior to other mainstream algorithms and consistent with the outcomes of traditional theoretical spectrum fitting methods. In the future, our algorithm will be applied to large-scale parameter estimations on data from the China Space Station Survey Telescope and the Dark Energy Spectroscopic Instrument. [ABSTRACT FROM AUTHOR] |
| Copyright of Astronomy & Astrophysics / Astronomie et Astrophysique is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: An adaptive parameter estimator for poor-quality spectral data of white dwarfs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Jiangchuan%22">Zhang, Jiangchuan</searchLink><br /><searchLink fieldCode="AR" term="%22Xie%2C+Duo%22">Xie, Duo</searchLink><br /><searchLink fieldCode="AR" term="%22Bu%2C+Yude%22">Bu, Yude</searchLink><br /><searchLink fieldCode="AR" term="%22Yi%2C+Zhenping%22">Yi, Zhenping</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Meng%22">Liu, Meng</searchLink><br /><searchLink fieldCode="AR" term="%22Kong%2C+Xiaoming%22">Kong, Xiaoming</searchLink> – Name: TitleSource Label: Source Group: Src Data: Astronomy & Astrophysics / Astronomie et Astrophysique; 2025, Vol. 704, p1-13, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22White+dwarf+stars%22">White dwarf stars</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis+instruments%22">Spectrum analysis instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+estimation+%28Statistics%29%22">Adaptive estimation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Stellar+evolution%22">Stellar evolution</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: White dwarfs represent the end stage for 97% of stars, making precise parameter measurement crucial for understanding stellar evolution. Traditional estimation methods involve fitting spectra or photometry, which require high-quality data. In recent years, machine learning has played a crucial role in processing spectral data due to its speed, automation, and accuracy. However, two common issues have been identified. First, most studies rely on data with high signal-to-noise ratios (S/N > 10), leaving many poor-quality datasets underutilized. Second, existing machine learning models, primarily based on convolutional and recurrent networks and their variants, fail to simultaneously capture both local spatial and long-range sequential spectral information. To address these challenges, we designed the estimator network (EstNet), an advanced algorithm integrating multiple techniques, including residual networks, squeeze and excitation attention, gated recurrent units, adaptive loss, and Monte Carlo dropout. A total of 5965 poor-quality white dwarf spectra (R~1800, S/N~1.17) were used for the experiments. EstNet achieved average percentage errors of 13.81% for effective temperature and 3.92% for surface gravity on the test set. These results are superior to other mainstream algorithms and consistent with the outcomes of traditional theoretical spectrum fitting methods. In the future, our algorithm will be applied to large-scale parameter estimations on data from the China Space Station Survey Telescope and the Dark Energy Spectroscopic Instrument. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Astronomy & Astrophysics / Astronomie et Astrophysique is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1051/0004-6361/202451015 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: White dwarf stars Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Spectrum analysis instruments Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Data quality Type: general – SubjectFull: Adaptive estimation (Statistics) Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Stellar evolution Type: general Titles: – TitleFull: An adaptive parameter estimator for poor-quality spectral data of white dwarfs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Jiangchuan – PersonEntity: Name: NameFull: Xie, Duo – PersonEntity: Name: NameFull: Bu, Yude – PersonEntity: Name: NameFull: Yi, Zhenping – PersonEntity: Name: NameFull: Liu, Meng – PersonEntity: Name: NameFull: Kong, Xiaoming IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00046361 Numbering: – Type: volume Value: 704 Titles: – TitleFull: Astronomy & Astrophysics / Astronomie et Astrophysique Type: main |
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