BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies
We introduce BAYES-LOSVD, a novel implementation of the non-parametric extraction of line-of-sight velocity distributions (LOSVDs) in galaxies. We employ bayesian inference to obtain robust LOSVDs and associated uncertainties. Our method relies on principal component analysis to reduce the dimension...
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ftdatacite:10.48550/arxiv.2011.12023 2023-05-15T18:12:28+02:00 BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies Falcon-Barroso, J. Martig, M. 2020 https://dx.doi.org/10.48550/arxiv.2011.12023 https://arxiv.org/abs/2011.12023 unknown arXiv https://dx.doi.org/10.1051/0004-6361/202039624 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Astrophysics of Galaxies astro-ph.GA FOS Physical sciences article-journal Article ScholarlyArticle Text 2020 ftdatacite https://doi.org/10.48550/arxiv.2011.12023 https://doi.org/10.1051/0004-6361/202039624 2022-03-10T15:10:31Z We introduce BAYES-LOSVD, a novel implementation of the non-parametric extraction of line-of-sight velocity distributions (LOSVDs) in galaxies. We employ bayesian inference to obtain robust LOSVDs and associated uncertainties. Our method relies on principal component analysis to reduce the dimensionality of the base of templates required for the extraction and thus increase the performance of the code. In addition, we implement several options to regularise the output solutions. Our tests, conducted on mock spectra, confirm the ability of our approach to model a wide range of LOSVD shapes, overcoming limitations of the most widely used parametric methods (e.g. Gauss-Hermite expansion). We present examples of LOSVD extractions for real galaxies with known peculiar LOSVD shapes, i.e. NGC4371, IC0719 and NGC4550, using MUSE and SAURON integral-field unit (IFU) data. Our implementation can also handle data from other popular IFU surveys (e.g. ATLAS3D, CALIFA, MaNGA, SAMI). Details of the code and relevant documentation are freely available to the community in the dedicated repositories. : 13 pages, 7 figures. Accepted for publication in Astronomy & Astrophysics. Public repository with the code can be found at: https://github.com/jfalconbarroso/BAYES-LOSVD Article in Journal/Newspaper sami DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Astrophysics of Galaxies astro-ph.GA FOS Physical sciences |
spellingShingle |
Astrophysics of Galaxies astro-ph.GA FOS Physical sciences Falcon-Barroso, J. Martig, M. BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies |
topic_facet |
Astrophysics of Galaxies astro-ph.GA FOS Physical sciences |
description |
We introduce BAYES-LOSVD, a novel implementation of the non-parametric extraction of line-of-sight velocity distributions (LOSVDs) in galaxies. We employ bayesian inference to obtain robust LOSVDs and associated uncertainties. Our method relies on principal component analysis to reduce the dimensionality of the base of templates required for the extraction and thus increase the performance of the code. In addition, we implement several options to regularise the output solutions. Our tests, conducted on mock spectra, confirm the ability of our approach to model a wide range of LOSVD shapes, overcoming limitations of the most widely used parametric methods (e.g. Gauss-Hermite expansion). We present examples of LOSVD extractions for real galaxies with known peculiar LOSVD shapes, i.e. NGC4371, IC0719 and NGC4550, using MUSE and SAURON integral-field unit (IFU) data. Our implementation can also handle data from other popular IFU surveys (e.g. ATLAS3D, CALIFA, MaNGA, SAMI). Details of the code and relevant documentation are freely available to the community in the dedicated repositories. : 13 pages, 7 figures. Accepted for publication in Astronomy & Astrophysics. Public repository with the code can be found at: https://github.com/jfalconbarroso/BAYES-LOSVD |
format |
Article in Journal/Newspaper |
author |
Falcon-Barroso, J. Martig, M. |
author_facet |
Falcon-Barroso, J. Martig, M. |
author_sort |
Falcon-Barroso, J. |
title |
BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies |
title_short |
BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies |
title_full |
BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies |
title_fullStr |
BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies |
title_full_unstemmed |
BAYES-LOSVD: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies |
title_sort |
bayes-losvd: a bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies |
publisher |
arXiv |
publishDate |
2020 |
url |
https://dx.doi.org/10.48550/arxiv.2011.12023 https://arxiv.org/abs/2011.12023 |
genre |
sami |
genre_facet |
sami |
op_relation |
https://dx.doi.org/10.1051/0004-6361/202039624 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.48550/arxiv.2011.12023 https://doi.org/10.1051/0004-6361/202039624 |
_version_ |
1766184998388367360 |