Stellar evolution

@article{eggleton1972composition,
    author = "Eggleton, P. P.",
    title = "Composition Changes During Stellar Evolution",
    year = "1972",
    journal = "Monthly Notices of the Royal Astronomical Society",
    url = "https://doi.org/10.1093/mnras/156.3.361",
    doi = "10.1093/mnras/156.3.361",
    number = "3",
    pages = "361-376",
    volume = "156"
}

@misc{jastrow1979red2,
    author = "Jastrow, R",
    title = "Red Giants and White Dwarfs [New ed.]",
    year = "1979",
    howpublished = "New York, Norton",
    note = "talkorigins\_source = {true}; raw\_reference = {Jastrow, R., 1979, Red Giants and White Dwarfs [New ed.]: New York, Norton.}"
}

@misc{strom1979the4,
    author = "Strom, S. E. and Strom, K. M",
    title = "The evolution of disk galaxies",
    year = "1979",
    howpublished = "Scientific American, v. 240, no. 4, p. 72-82",
    note = "talkorigins\_source = {true}; raw\_reference = {Strom, S. E., and Strom, K. M., 1979, The evolution of disk galaxies: Scientific American, v. 240, no. 4, p. 72-82.}"
}

The 14 C production rate in the upper atmosphere changes with time because the galactic cosmic-ray flux responsible for 14 C production is modulated by the changes in solar wind magnetic properties. The resulting changes in the atmospheric 14 C level are recorded in tree rings and are used to calculate past 14 C production rates from a carbon reservoir model that describes terrestrial carbon exchange between the atmosphere, ocean, and biosphere. These past 14 C production rate changes are compared with 14 C production rates determined from 20th-century neutron flux measurements, and a theory relating 14 C production and solar variability, as given by geomagnetic Aa indices and sunspot numbers, is developed. This theory takes into account long-term solar changes that were previously neglected. The 860-year 14 C record indicates three episodes when sunspots apparently were absent: A.D. 1654 to 1714 (Maunder minimum), 1416 to 1534 (Spörer minimum), and 1282 to 1342 (Wolf minimum). A less precisely defined minimum occurred near A.D. 1040. The part of this record after A.D. 1645 correlates well with the basic features of the historical record of sunspot numbers. The magnitude of the calculated 14 C production rates points to a further increase in cosmic-ray flux when sunspots are absent. This flux was greatest during the Spörer minimum. A record of approximate sunspot numbers and Aa indices for the current millennium is also presented.

@article{stuiver1980changes,
    author = "Stuiver, Minze and Quay, Paul D.",
    title = "Changes in Atmospheric Carbon-14 Attributed to a Variable Sun",
    year = "1980",
    journal = "Science",
    abstract = "The 14 C production rate in the upper atmosphere changes with time because the galactic cosmic-ray flux responsible for 14 C production is modulated by the changes in solar wind magnetic properties. The resulting changes in the atmospheric 14 C level are recorded in tree rings and are used to calculate past 14 C production rates from a carbon reservoir model that describes terrestrial carbon exchange between the atmosphere, ocean, and biosphere. These past 14 C production rate changes are compared with 14 C production rates determined from 20th-century neutron flux measurements, and a theory relating 14 C production and solar variability, as given by geomagnetic Aa indices and sunspot numbers, is developed. This theory takes into account long-term solar changes that were previously neglected. The 860-year 14 C record indicates three episodes when sunspots apparently were absent: A.D. 1654 to 1714 (Maunder minimum), 1416 to 1534 (Spörer minimum), and 1282 to 1342 (Wolf minimum). A less precisely defined minimum occurred near A.D. 1040. The part of this record after A.D. 1645 correlates well with the basic features of the historical record of sunspot numbers. The magnitude of the calculated 14 C production rates points to a further increase in cosmic-ray flux when sunspots are absent. This flux was greatest during the Spörer minimum. A record of approximate sunspot numbers and Aa indices for the current millennium is also presented.",
    url = "https://doi.org/10.1126/science.207.4426.11",
    doi = "10.1126/science.207.4426.11",
    number = "4426",
    pages = "11-19",
    volume = "207"
}

@misc{stuvier1980changes5,
    author = "Stuvier, M. and Quay, P. D",
    title = "Changes in atmospheric carbon-14 attributed to a variable sun",
    year = "1980",
    howpublished = "Science, v. 207, p. 11-19",
    note = "talkorigins\_source = {true}; raw\_reference = {Stuvier, M., and Quay, P. D., 1980, Changes in atmospheric carbon-14 attributed to a variable sun: Science, v. 207, p. 11-19.}"
}

@incollection{russell1983astronomical3,
    author = "Russell, J. L",
    editor = "Wilson, D. B.",
    title = "Astronomical Creation: The Evolution of Stars and Planets",
    year = "1983",
    booktitle = "Did the Devil Make Darwin Do It? Modern Perspectives on the Creation/Evolution Controversy",
    publisher = "Ames, Iowa, Iowa University Press, p. 46-54",
    note = "talkorigins\_source = {true}; raw\_reference = {Russell, J. L., 1983, Astronomical Creation: The Evolution of Stars and Planets, in Wilson, D. B., ed., Did the Devil Make Darwin Do It? Modern Perspectives on the Creation/Evolution Controversy: Ames, Iowa, Iowa University Press, p. 46-54.}"
}

@incollection{fernie1984cepheid,
    author = "Fernie, J. D.",
    title = "Cepheid Period Changes and Stellar Evolution",
    year = "1984",
    booktitle = "Observational Tests of the Stellar Evolution Theory",
    url = "https://doi.org/10.1007/978-94-010-9570-9\_77",
    doi = "10.1007/978-94-010-9570-9\_77",
    pages = "441-444"
}

@article{walker1985atmospheric,
    author = "WALKER, J. C. G.",
    title = "Atmospheric Evolution: The Carbon Cycle and Atmospheric CO2.",
    year = "1985",
    journal = "Science",
    url = "https://doi.org/10.1126/science.230.4722.163-a",
    doi = "10.1126/science.230.4722.163-a",
    number = "4722",
    pages = "163-164",
    volume = "230"
}

@book{cohen1988in1,
    author = "Cohen, M",
    title = "In Darkness Born",
    year = "1988",
    publisher = "The Story of Star Formation: Cambridge, Cambridge University Press",
    note = "talkorigins\_source = {true}; raw\_reference = {Cohen, M., 1988, In Darkness Born: The Story of Star Formation: Cambridge, Cambridge University Press.}"
}

@misc{crossref2007astronomical,
    title = "Astronomical Refraction (Atmospheric Refraction)",
    year = "2007",
    booktitle = "Van Nostrand's Scientific Encyclopedia",
    url = "https://doi.org/10.1002/0471743984.vse9166",
    doi = "10.1002/0471743984.vse9166"
}

@incollection{lin2014changes,
    author = "Lin, Hua",
    title = "Changes in Atmospheric Carbon Dioxide",
    year = "2014",
    booktitle = "Global Environmental Change",
    url = "https://doi.org/10.1007/978-94-007-5784-4\_48",
    doi = "10.1007/978-94-007-5784-4\_48",
    pages = "61-67"
}

There is a hugh number of stars (~ few hundred billions) of different ages, sizes and masses in our galaxy, the Milky Way, and billions of other galaxies in the Universe. It was extremely challenging for astronomers to classify them into different groups to understand their properties precisely. In general, stars remains in the main sequence phases in the HR diagram for the largest fraction of its life time because it maintains hydro-static equilibrium during this phase. Stars of diverse mass range pass through different evolutionary phases. Some of these end their lives as catastrophic explosions not understood well so far and have a great potential to understand the overall evolution process of stars and in turn evolution of the Universe. Ground and space-based multi-wavelength observations of these objects are crucial to understand them in terms of laws of physics. In near future, advancement of technology demands extensive use of artificial intelligence, neural networks, robotics to understand astronomical observations in a better way.

@article{pandey2021stellar,
    author = "Pandey, A.",
    title = "STELLAR EVOLUTION AND THEIR ASTRONOMICAL OBSERVATIONS",
    year = "2021",
    journal = "Revista Mexicana de Astronomía y Astrofísica Serie de Conferencias",
    abstract = "There is a hugh number of stars (\textasciitilde\ few hundred billions) of different ages, sizes and masses in our galaxy, the Milky Way, and billions of other galaxies in the Universe. It was extremely challenging for astronomers to classify them into different groups to understand their properties precisely. In general, stars remains in the main sequence phases in the HR diagram for the largest fraction of its life time because it maintains hydro-static equilibrium during this phase. Stars of diverse mass range pass through different evolutionary phases. Some of these end their lives as catastrophic explosions not understood well so far and have a great potential to understand the overall evolution process of stars and in turn evolution of the Universe. Ground and space-based multi-wavelength observations of these objects are crucial to understand them in terms of laws of physics. In near future, advancement of technology demands extensive use of artificial intelligence, neural networks, robotics to understand astronomical observations in a better way.",
    url = "https://doi.org/10.22201/ia.14052059p.2021.53.29",
    doi = "10.22201/ia.14052059p.2021.53.29",
    pages = "147-150",
    volume = "53"
}

Context. The rapid expansion of large-scale spectroscopic surveys has highlighted the need to use automatic methods to extract information about the properties of stars with the greatest efficiency and accuracy, and also to optimise the use of computational resources. Aims. We developed a method based on generative adversarial networks (GANs) to disentangle the physical (effective temperature and gravity) and chemical (metallicity and overabundance of α elements with respect to iron) atmospheric properties in astronomical spectra. Using a projection of the stellar spectra, commonly called latent space, in which the contribution due to one or several main stellar physicochemical properties is minimised while others are enhanced, it was possible to maximise the information related to certain properties. This could then be extracted using artificial neural networks (ANNs) as regressors, with a higher accuracy than a reference method based on the use of ANNs that had been trained with the original spectra. Methods. Our model utilises auto-encoders, comprising two ANNs: an encoder and a decoder that transform input data into a low-dimensional representation known as latent space. It also uses discriminators, which are additional neural networks aimed at transforming the traditional auto-encoder training into an adversarial approach. This is done to reinforce the astrophysical parameters or disentangle them from the latent space. We describe our Generative Adversarial Networks for Disentangling and Learning Framework (GANDALF) tool in this article. It was developed to define, train, and test our GAN model with a web framework to show visually how the disentangling algorithm works. It is open to the community in Github. Results. We demonstrate the performance of our approach for retrieving atmospheric stellar properties from spectra using Gaia Radial Velocity Spectrograph (RVS) data from DR3. We used a data-driven perspective and obtained very competitive values, all within the literature errors, and with the advantage of an important dimensionality reduction of the data to be processed.

@article{manteiga2025disentangling,
    author = "Manteiga, M. and Santoveña, R. and Álvarez, M. A. and Dafonte, C. and Penedo, M. G. and Navarro, S. and Corral, L.",
    title = "Disentangling stellar atmospheric parameters in astronomical spectra using generative adversarial neural networks",
    year = "2025",
    journal = "Astronomy \& Astrophysics",
    abstract = "Context. The rapid expansion of large-scale spectroscopic surveys has highlighted the need to use automatic methods to extract information about the properties of stars with the greatest efficiency and accuracy, and also to optimise the use of computational resources. Aims. We developed a method based on generative adversarial networks (GANs) to disentangle the physical (effective temperature and gravity) and chemical (metallicity and overabundance of α elements with respect to iron) atmospheric properties in astronomical spectra. Using a projection of the stellar spectra, commonly called latent space, in which the contribution due to one or several main stellar physicochemical properties is minimised while others are enhanced, it was possible to maximise the information related to certain properties. This could then be extracted using artificial neural networks (ANNs) as regressors, with a higher accuracy than a reference method based on the use of ANNs that had been trained with the original spectra. Methods. Our model utilises auto-encoders, comprising two ANNs: an encoder and a decoder that transform input data into a low-dimensional representation known as latent space. It also uses discriminators, which are additional neural networks aimed at transforming the traditional auto-encoder training into an adversarial approach. This is done to reinforce the astrophysical parameters or disentangle them from the latent space. We describe our Generative Adversarial Networks for Disentangling and Learning Framework (GANDALF) tool in this article. It was developed to define, train, and test our GAN model with a web framework to show visually how the disentangling algorithm works. It is open to the community in Github. Results. We demonstrate the performance of our approach for retrieving atmospheric stellar properties from spectra using Gaia Radial Velocity Spectrograph (RVS) data from DR3. We used a data-driven perspective and obtained very competitive values, all within the literature errors, and with the advantage of an important dimensionality reduction of the data to be processed.",
    url = "https://doi.org/10.1051/0004-6361/202451786",
    doi = "10.1051/0004-6361/202451786",
    pages = "A326",
    volume = "694"
}