El estudio de las estrellas en la era del Big Data
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abr 11, 2023
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Jaime Klapp
http://orcid.org/0000-0003-1828-9183
Celia Rosa Fierro-Santillán
http://orcid.org/0000-0002-6129-5346
Leonardo Di G. Sigalotti
http://orcid.org/0000-0001-8043-0825
http://orcid.org/0000-0003-1828-9183
Celia Rosa Fierro-Santillán
http://orcid.org/0000-0002-6129-5346
Leonardo Di G. Sigalotti
http://orcid.org/0000-0001-8043-0825
Resumen
Se aborda el problema de correlacionar modelos teóricos con espectros electromagnéticos observados en estrellas masivas en el contexto del gran volumen de información astronómica disponible actualmente. Se analizan las características de los códigos de atmósferas estelares y el proceso de modelado y ajuste del espectro observado de una estrella, los recursos de cómputo que se consumen en dicho proceso, así como el problema de los modelos desechados o almacenados en forma no estructurada. Se concluye que en el siglo XXI la forma de realizar investigación astronómica ha cambiado radicalmente al incorporar el enfoque Big Data y los observatorios virtuales.
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KLAPP, Jaime; FIERRO-SANTILLÁN, Celia Rosa; SIGALOTTI, Leonardo Di G..
El estudio de las estrellas en la era del Big Data.
CIENCIA ergo-sum, [S.l.], v. 31, n. 2, abr. 2023.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/18108>. Fecha de acceso: 29 mayo 2023
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Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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Universidad Nacional Autónoma de México, Atlas of CMFGEN Models for OB Massive Stars. http://www.astroscu.unam.mx/atlas/index.html
National Aeronautics and Space Administration Goddard Space Flight Center, Exoplanet Modeling and Analysis Center. https://emac.gsfc.nasa.gov/
International Virtual Observatory Alliance. IVOA. https://www.ivoa.net/
Hamann D. M. & Hamann W.-R. (2004), PoWR - The Potsdam Wolf-Rayet Models. https://www.astro.physik.uni-potsdam.de/~wrh/PoWR/powrgrid1.php#obgridstart.
Research School of Astronomy and Astrophysics, The Australian National University, Sky Mapper Southern Sky Survey. https://skymapper.anu.edu.au/
Alfred P. Sloan Foundation, U.S. Departament of Energy, Sloan Digital Sky Survey (SDSS). https://www.sdss.org/
Spanish Virtual Observatory , Spanish Virtual Observatory. https://svo.cab.inta-csic.es/main/index.php
Laboratoire Montpellier Univers & Particules, Université Montpellier-CNRS, The Pollux Database of Stellar Spectra. http://npollux.lupm.univ-montp2.fr/
Spanish Virtual Observatory , VO SED Analyzer. http://svo2.cab.inta-csic.es/theory/vosa/index.php
Cheng L., Zhang F., Kang X., Wang L. (2018), A new stellar spectrum interpolation algorithm and its application to Yunnan-III evolutionary population synthesis models, Monthly Notices of the Royal Astronomical Society, 476, 4071-4084. https://doi.org/10.1093/mnras/sty373
Dafonte C., Fustes D., Manteiga M., Garabato D., Álvarez M. A., Ulla A., Allende Prieto C. (2016), On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra, Astronomy & Astrophysics, 594, A68 1-10. https://doi.org/10.1051/0004-6361/201527045
Fierro, C. R., Borissova, J., Zsargó, J., Díaz-Azuara, A., Kurtev, R., Georgiev, L., Ramírez Alegría, S. & Peñaloza, F. (2015), Atlas of CMFGEN Models for OB Massive Stars, Publications of the Astronomical Society of the Pacific, 127, 428-436. https://iopscience.iop.org/article/10.1086/681703
Fierro-Santillán, C. R., Zsargó, J., Klapp, J., Díaz-Azuara, S.A., Arrieta, A., Arias, L. & Sigalotti, L. Di G. (2018), FITspec: A New Algorithm for the Automated Fit of Synthetic Stellar Spectra for OB Stars, The Astrophysical Journal Supplement, 236, 38-53. https://iopscience.iop.org/article/10.3847/1538-4365/aabd3a
Gräfener, G., Koesterke, L., & Hamann, W.-R. (2002), Line-blanketed model atmospheres for WR stars, Astronomy & Astrophysics, 387, 244-257. https://doi.org/10.1051/0004-6361:20020269
Hamann, W.-R., & Gräfener, G. (2003), A temperature correction method for expanding atmospheres, Astronomy & Astrophysics, 410, 993-1000. https://doi.org/10.1051/0004-6361:20031308
Hamann, W.-R., & Gräfener, G. (2004), Grids of model spectra for WN stars, ready for use, Astronomy & Astrophysics, 427, 697-704. https://doi.org/10.1051/0004-6361:20040506
Hillier, D. J., & Miller, D. L. (1998), The Treatment of Non-LTE Line Blanketing in Spherically Expanding Outflows, The Astrophysical Journal Supplement, 496, 407-427. https://iopscience.iop.org/article/10.1086/305350
Hubeny, I., & Lanz, T. (1995), Non-LTE Line-Blanketed Model Atmospheres of Hot Stars. I. Hybrid Complete Linearization/Accelerated Lambda Iteration Method, The Astrophysical Journal, 439, 875-904. https://articles.adsabs.harvard.edu/full/1995ApJ...439..875H
Palacios A., Gebran M., Josselin E., Martins F., Plez B., Belmas M., Lèbre A. (2010), POLLUX: a database of synthetic stellar spectra, Astronomy & Astrophysics, 516, A13 1-8. https://doi.org/10.1051/0004-6361/200913932
Sander, A., Shenar, T., Hainich, R., Gimenez-García A., Todt H., Hamman, W. -R. (2015), On the consistent treatment of the quasi-hydrostatic layers in hot star atmospheres, Astronomy & Astrophysics, 577, A13 1-13. https://doi.org/10.1051/0004-6361/201425356
Santolaya-Rey, A. E., Puls, J., & Herrero, A. (1997), Atmospheric NLTE-models for the spectroscopic analysis of luminous blue stars with winds, Astronomy & Astrophysics, 323, 488-512. https://articles.adsabs.harvard.edu/full/1997A%26A...323..488S
Sharma K., Kembhavi A., Kembhavi A., Sivarani T., Abraham S., Vaghmare, K. (2019), Detecting Outliers in SDSS using Convolutional Neural Network, Monthly Notices of the Royal Astronomical Society, 88, 174-181. https://popups.uliege.be/0037-9565/index.php?id=8811
Sharma K., Singth H.P., Gupta R., Kembhavi A., Vaghmare K., Shi J., Zhao Y., Zhang J. & Wu Y. (2020), Detecting Outliers in SDSS using Convolutional Neural Network, Monthly Notices of the Royal Astronomical Society, 88, 174-181. https://doi.org/10.25518/0037-9565.8811
Ting Y.-S., Conroy C., Rix H.-W., Cargile P. (2019), The Payne: Self-consistent ab initio Fitting of Stellar Spectra, The Astrophysical Journal Supplement, 879, 69, 1-22. https://iopscience.iop.org/article/10.3847/1538-4357/ab2331
Zsargó, J., Fierro-Santillán, C. R., Klapp, J., Arrieta, A., Arias, L., Valencia, J. M. , Sigalotti, L. Di G., Hareter, M. & Puebla, R. E. (2020), Creating and using large grids of precalculated model atmospheres for a rapid analysis of stellar spectra, Astronomy & Astrophysics, 643, A88 1-20. https://doi.org/10.1051/0004-6361/202038066
PÁGINAS WEB
Universidad Nacional Autónoma de México, Atlas of CMFGEN Models for OB Massive Stars. http://www.astroscu.unam.mx/atlas/index.html
National Aeronautics and Space Administration Goddard Space Flight Center, Exoplanet Modeling and Analysis Center. https://emac.gsfc.nasa.gov/
International Virtual Observatory Alliance. IVOA. https://www.ivoa.net/
Hamann D. M. & Hamann W.-R. (2004), PoWR - The Potsdam Wolf-Rayet Models. https://www.astro.physik.uni-potsdam.de/~wrh/PoWR/powrgrid1.php#obgridstart.
Research School of Astronomy and Astrophysics, The Australian National University, Sky Mapper Southern Sky Survey. https://skymapper.anu.edu.au/
Alfred P. Sloan Foundation, U.S. Departament of Energy, Sloan Digital Sky Survey (SDSS). https://www.sdss.org/
Spanish Virtual Observatory , Spanish Virtual Observatory. https://svo.cab.inta-csic.es/main/index.php
Laboratoire Montpellier Univers & Particules, Université Montpellier-CNRS, The Pollux Database of Stellar Spectra. http://npollux.lupm.univ-montp2.fr/
Spanish Virtual Observatory , VO SED Analyzer. http://svo2.cab.inta-csic.es/theory/vosa/index.php