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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.03816 |
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| _version_ | 1866913054069882880 |
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| author | Jespersen, Christian K. Melchior, Peter Spergel, David N. Goulding, Andy D. Hahn, ChangHoon Iyer, Kartheik G. |
| author_facet | Jespersen, Christian K. Melchior, Peter Spergel, David N. Goulding, Andy D. Hahn, ChangHoon Iyer, Kartheik G. |
| contents | Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle correlations between physical processes to accurately predict infrared (IR) WISE photometry from a neural summary of optical SDSS spectra. The model achieves accuracies of $χ^2_N \approx 1$ for all photometric bands in WISE, as well as good colors. We are able to tightly constrain typically IR-derived properties, e.g., the bolometric luminosities of AGN and dust parameters such as $\mathrm{q_{PAH}}$. We also test whether current SED-fitting methods reproduce such panchromatic relations, but find their predictions biased and overconfident, likely due to model misspecification, with correlated biases in star-formation rates and AGN luminosities being most evident. To help improve SED models, we determine which features of the optical spectrum are responsible for our improved predictions, and identify several lines (CaII, SrII, FeI, [OII] and H$α$), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03816 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Optical and Infrared Are Connected Jespersen, Christian K. Melchior, Peter Spergel, David N. Goulding, Andy D. Hahn, ChangHoon Iyer, Kartheik G. Astrophysics of Galaxies Machine Learning Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle correlations between physical processes to accurately predict infrared (IR) WISE photometry from a neural summary of optical SDSS spectra. The model achieves accuracies of $χ^2_N \approx 1$ for all photometric bands in WISE, as well as good colors. We are able to tightly constrain typically IR-derived properties, e.g., the bolometric luminosities of AGN and dust parameters such as $\mathrm{q_{PAH}}$. We also test whether current SED-fitting methods reproduce such panchromatic relations, but find their predictions biased and overconfident, likely due to model misspecification, with correlated biases in star-formation rates and AGN luminosities being most evident. To help improve SED models, we determine which features of the optical spectrum are responsible for our improved predictions, and identify several lines (CaII, SrII, FeI, [OII] and H$α$), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled. |
| title | The Optical and Infrared Are Connected |
| topic | Astrophysics of Galaxies Machine Learning |
| url | https://arxiv.org/abs/2503.03816 |