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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.05909 |
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| _version_ | 1866917255506296832 |
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| author | Qiu, Chen Napolitano, Nicola R. Li, Rui Fang, Yuedong Tortora, Crescenzo Shen, Shiyin Ho, Luis C. Lin, Weipeng Wei, Leyao Li, Ran Fan, Zuhui Wang, Yang Li, Guoliang Zhan, Hu Liu, Dezi |
| author_facet | Qiu, Chen Napolitano, Nicola R. Li, Rui Fang, Yuedong Tortora, Crescenzo Shen, Shiyin Ho, Luis C. Lin, Weipeng Wei, Leyao Li, Ran Fan, Zuhui Wang, Yang Li, Guoliang Zhan, Hu Liu, Dezi |
| contents | Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the galaxy morphology and understand its evolution across time. So far, high-quality data have allowed detailed B-D decomposition to redshift below 0.5, with limited excursions over small volumes at higher redshifts. Next-generation large sky space surveys in optical, e.g. from the China Space Station Telescope (CSST), and near-infrared, e.g. from the space EUCLID mission, will produce a gigantic leap in these studies as they will provide deep, high-quality photometric images over more than 15000 deg2 of the sky, including billions of galaxies. Here, we extend the use of the Galaxy Light profile neural Network (GaLNet) to predict 2-Sérsic model parameters, specifically from CSST data. We simulate point-spread function (PSF) convolved galaxies, with realistic B-D parameter distributions, on CSST mock observations to train the new GaLNet and predict the structural parameters (e.g. magnitude, effective radius, Sersic index, axis ratio, etc.) of both bulge and disk components. We find that the GaLNet can achieve very good accuracy for most of the B-D parameters down to an $r$-band magnitude of 23.5 and redshift $\sim$1. The best accuracy is obtained for magnitudes, implying accurate bulge-to-total (B/T) estimates. To further forecast the CSST performances, we also discuss the results of the 1-Sérsic GaLNet and show that CSST half-depth data will allow us to derive accurate 1-component models up to $r\sim$24 and redshift z$\sim$1.7. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_05909 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Galaxy Light profile neural Networks (GaLNets). II. Bulge-Disc decomposition in optical space-based observations Qiu, Chen Napolitano, Nicola R. Li, Rui Fang, Yuedong Tortora, Crescenzo Shen, Shiyin Ho, Luis C. Lin, Weipeng Wei, Leyao Li, Ran Fan, Zuhui Wang, Yang Li, Guoliang Zhan, Hu Liu, Dezi Astrophysics of Galaxies Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the galaxy morphology and understand its evolution across time. So far, high-quality data have allowed detailed B-D decomposition to redshift below 0.5, with limited excursions over small volumes at higher redshifts. Next-generation large sky space surveys in optical, e.g. from the China Space Station Telescope (CSST), and near-infrared, e.g. from the space EUCLID mission, will produce a gigantic leap in these studies as they will provide deep, high-quality photometric images over more than 15000 deg2 of the sky, including billions of galaxies. Here, we extend the use of the Galaxy Light profile neural Network (GaLNet) to predict 2-Sérsic model parameters, specifically from CSST data. We simulate point-spread function (PSF) convolved galaxies, with realistic B-D parameter distributions, on CSST mock observations to train the new GaLNet and predict the structural parameters (e.g. magnitude, effective radius, Sersic index, axis ratio, etc.) of both bulge and disk components. We find that the GaLNet can achieve very good accuracy for most of the B-D parameters down to an $r$-band magnitude of 23.5 and redshift $\sim$1. The best accuracy is obtained for magnitudes, implying accurate bulge-to-total (B/T) estimates. To further forecast the CSST performances, we also discuss the results of the 1-Sérsic GaLNet and show that CSST half-depth data will allow us to derive accurate 1-component models up to $r\sim$24 and redshift z$\sim$1.7. |
| title | Galaxy Light profile neural Networks (GaLNets). II. Bulge-Disc decomposition in optical space-based observations |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2306.05909 |