El estudio de las estrellas en la era del big data
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feb 20, 2024
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https://doi.org/10.30878/ces.v31n0a36
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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 en la actualidad. 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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Como citar
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, feb. 2024.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/18108>. Fecha de acceso: 25 jun. 2025
doi: https://doi.org/10.30878/ces.v31n0a36.
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Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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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
Hamann D. M., & Hamann W.-R. (2004). University of Postdam, PoWR - The Potsdam Wolf-Rayet Models. https://www.astro.physik.uni-potsdam.de/~wrh/PoWR/powrgrid1.php#obgridstart.
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
Puls, J., Urbaneja, M.A., Venero, R., et al. (2005). Astronomy & Astrophysics, 465, 669-698. https://ui.adsabs. harvard.edu/link_gateway/2005A%26A...435..669P/PUB_PDF
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. Bulletin de la Société Royale des Sciences de Liège, 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. Bulletin de la Société Royale des Sciences de Liège, 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(2). https://iopscience.iop.org/article/10.3847/1538-4357/ab2331
UNAM (Universidad Nacional Autónoma de México). (2015). Atlas of CMFGEN Models for OB Massive Stars. http://www.astroscu.unam.mx/atlas/index.html
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