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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2605.24093 |
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| author | Martín-Navarro, Ignacio Vazdekis, Alexandre de Arriba, Luis Peralta Asensio, Isaac Alonso Angeloudi, Eirini Navarro, Patricia Iglesias La Barbera, Francesco Fahrion, Katja Jerabkova, Tereza Beasley, Michael A. Falcón-Barroso, Jesús Huertas-Company, Marc Sánchez, Sebastián F. Jethwa, Prashin |
| author_facet | Martín-Navarro, Ignacio Vazdekis, Alexandre de Arriba, Luis Peralta Asensio, Isaac Alonso Angeloudi, Eirini Navarro, Patricia Iglesias La Barbera, Francesco Fahrion, Katja Jerabkova, Tereza Beasley, Michael A. Falcón-Barroso, Jesús Huertas-Company, Marc Sánchez, Sebastián F. Jethwa, Prashin |
| contents | The development of evolutionary stellar population models is central to interpreting observations of galaxies in terms of astrophysical quantities. Stellar population models must therefore be both accurate and compatible with inversion algorithms in order to extract meaningful information from the observed data. Here we present FASTAR, a fully differentiable stellar population synthesis code. Contrary to traditional, grid-based single stellar population models, FASTAR can be continuously evaluated at any age (between 20 Myr and 14 Gyr), metallicity (-2.5 < [M/H] < +0.3), and initial mass function (IMF). Changes in the IMF parameterization are straightforward, allowing for consistent conversions of colors, magnitudes, and mass-to-light ratios, as well as the synthesis of models under the assumption of arbitrary IMF functional forms. FASTAR provides detailed spectroscopic predictions over the MILES wavelength range (3,540-7,400 A) as well as more coarsely sampled spectral energy distributions across a wider 2,000-to-12,000 A, which can be directly convolved with any arbitrary set of photometric filters. FASTAR performs at the same level of state-of-the-art simple stellar population models benchmarked against observations of globular clusters and high signal-to-noise spectra of early-type galaxies, but it is faster, lighter, and more flexible. Moreover, its differentiable nature allows for a quantitative understanding of model behavior and uncertainties, as well as a natural framework for gradient descent inference algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24093 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FASTAR -- I. Continuous and differentiable evolutionary stellar population models Martín-Navarro, Ignacio Vazdekis, Alexandre de Arriba, Luis Peralta Asensio, Isaac Alonso Angeloudi, Eirini Navarro, Patricia Iglesias La Barbera, Francesco Fahrion, Katja Jerabkova, Tereza Beasley, Michael A. Falcón-Barroso, Jesús Huertas-Company, Marc Sánchez, Sebastián F. Jethwa, Prashin Astrophysics of Galaxies The development of evolutionary stellar population models is central to interpreting observations of galaxies in terms of astrophysical quantities. Stellar population models must therefore be both accurate and compatible with inversion algorithms in order to extract meaningful information from the observed data. Here we present FASTAR, a fully differentiable stellar population synthesis code. Contrary to traditional, grid-based single stellar population models, FASTAR can be continuously evaluated at any age (between 20 Myr and 14 Gyr), metallicity (-2.5 < [M/H] < +0.3), and initial mass function (IMF). Changes in the IMF parameterization are straightforward, allowing for consistent conversions of colors, magnitudes, and mass-to-light ratios, as well as the synthesis of models under the assumption of arbitrary IMF functional forms. FASTAR provides detailed spectroscopic predictions over the MILES wavelength range (3,540-7,400 A) as well as more coarsely sampled spectral energy distributions across a wider 2,000-to-12,000 A, which can be directly convolved with any arbitrary set of photometric filters. FASTAR performs at the same level of state-of-the-art simple stellar population models benchmarked against observations of globular clusters and high signal-to-noise spectra of early-type galaxies, but it is faster, lighter, and more flexible. Moreover, its differentiable nature allows for a quantitative understanding of model behavior and uncertainties, as well as a natural framework for gradient descent inference algorithms. |
| title | FASTAR -- I. Continuous and differentiable evolutionary stellar population models |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2605.24093 |